AI has become ubiquitous today from personal devices to enterprise applications, you see them everywhere. The advent of IoT clubbed with rising demand for data privacy, low power, low latency, and bandwidth constraints has increasingly pushed for AI models to be running at the edge instead of the cloud. According to Grand View Research, the global edge artificial intelligence chips market was valued at USD 1.8 billion in 2019 and is expected to grow at a CAGR of 21.3 percent from 2020 to 2027. On this onset, Google introduced Edge TPU, also known as Coral TPU, which is its purpose-built ASIC for running AI at edge. It’s designed to give an excellent performance while taking up minimal space and power. When we train an AI model, we end up with AI models that have high storage requirements and GPU processing power. We cannot execute them on devices that have low memory and processing footprints. TensorFlow Lite is useful in this situation. TensorFlow Lite is an open-source deep learning framework that runs on the Edge TPU and allows for on-device inference and AI model execution. Also note that TensorFlow Lite is only for executing inference on the edge, not for training a model. For training an AI model, we must use TensorFlow.

Combining Edge TPU and TensorFlow Lite

When we talk about deploying an AI model on Edge TPU, we just cannot deploy any AI model.
The Edge TPU supports NN (Neural Network) operations and designs to enable high-speed neural network performance with low power consumption. Apart from specific networks, it only supports 8-bit quantized and compiled TensorFlow Lite models for Edge TPU.

For a quick summary, TensorFlow Lite is a lightweight version of TensorFlow specially designed for mobile and embedded devices. It achieves low latency results with a small storage size. There is a TensorFlow Lite converter that allows converting a TensorFlow-based AI model file (. pb) to a TensorFlow Lite file (.tflite). Below is a standard workflow for deploying applications on Edge TPU

Let’s look at some interesting real-world applications that can be built using TensorFlow Lite on edge TPU.

Human Detection and Counting

This solution has so many practical applications, especially in malls, retail, government offices, banks, and enterprises. One may wonder what one can do with detecting and counting humans. Data now has the value of time and money. Let us see how the insights from human detection and counting can be used.

Estimating Footfalls

For the retail industry, this is important as it gives an idea if their stores are doing well. Whether their displays are attracting customers to enter the shops. It also helps them to know if they need to increase or decrease support staff. For other organizations, they help in taking adequate security measures for people.

Crowd Analytics and Queue Management

For govt offices and enterprises, queue management via human detection and counting helps them manage longer queues and save people’s time. Studying queues can attribute to individual and organizations’ performance. Crowd detection can help analyze crowd alerts for emergencies, security incidents, etc., and take appropriate actions. Such solutions give the best results when deployed on edge, as required actions can be taken close to real-time.

Age and Gender-based Targeted Advertisements

This solution mainly has practical applications in the retail and advertisement industry. Imagine you walking towards the advertisement display which was showing a women’s shoe ad and then suddenly the advertisement changes to a male’s shoe ad as it determined you being male. Targeted advertisements help retailers and manufacturers target their products better and create brand awareness that a normal person would never get to see in his busy life.

This cannot be restricted to only advertisements, age and gender detection can also help businesses in taking quick decisions by managing appropriate support staff in retail stores, what age and gender people prefer visiting your store, businesses, etc. All this is more powerful and effective if you are very quick to determine and act. So, even more, a reason to have this solution on Edge TPU.

Face Recognition

The very first face recognition system was built in 1970, and to date this is still being developed, being made more robust and effective. The main advantage of having face recognition on edge is real-time recognition. Another advantage is having face encryption and feature extraction on edge, and just sending encrypted and extracted data to the cloud for matching, thereby protecting PII level privacy of face images (as you don’t save face images on edge and cloud) and complying with stringent privacy laws.

Edge TPU combined with the TensorFlow Lite framework opens several edges AI applications opportunities. As the framework is open-source the Open-Source Software (OSS) community also supports it, making it even more popular for machine learning use cases. The overall platform of TensorFlow Lite enhances the environment for the growth of edge applications for embedded and IoT devices.

At Softnautics, we provide AI engineering and machine learning services and solutions with expertise on edge platforms (TPU, Rpi, FPGA), NN compiler for the edge, cloud platforms accelerators like AWS, Azure, AMD, and tools like TensorFlow, TensorFlow Lite, Docker, GIT, AWS DeepLens, Jetpack SDK, and many more targeted for domains like Automotive, Multimedia, Industrial IoT, Healthcare, Consumer, and Security-Surveillance. Softnautics helps businesses in building high-performance cloud and edge-based ML solutions like object/lane detection, face/gesture recognition, human counting, key-phrase/voice command detection, and more across various platforms.

Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.

Contact us at business@softnautics.com for any queries related to your solution or for consultancy.

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Machine Learning based facial recognition is a method of utilizing the face to identify or confirm one’s identity. Persons can be identified in pictures, films, or real time using facial recognition technology. Facial recognition has traditionally functioned in the same way as other biometric methods including voice recognition, eye irises, and fingerprint identification.

The growing use of facial recognition technology in a variety of applications is propelling the industry forward. In case of security, authorities are employing this technology to verify a passenger’s identity, particularly at airports. Face recognition software is also being used by law enforcement agencies to scan faces taken on CCTV and locate the suspect. Smartphones are another area of application where the technology has seen widespread adoption where the software is used to unlock the phone and verify payment information. As in the case of automotives self-driving cars are the focus of using this technology to unlock the car and act as the key to start/ stop the car. According to a report published by markets & markets group, the global facial recognition market is expected to grow at a CAGR of 17.2 percent over the forecast period, from USD 3.8 billion in 2020 to USD 8.5 billion in 2025.

Facial Recognition Technology Working Mechanism

A computer examines visual data and searches for a specified set of indicators, such as a person’s head shape, depth of their eyelids, etc. A database of facial markers is built, and an image of a face that matches the database’s essential threshold of resemblance suggests a possible match. Face recognition technologies, such as machine vision, modelling and reconstruction, and analytics, require the utilization of advanced algorithms in the areas of Machine Learning – Deep Learning and CNN (Convolutional Neural Network), which is growing at an exponential rate.

As facial recognition technology has progressed, a variety of systems for mapping faces and storing facial data have evolved based on Computer Vision, Deep Learning each with various degrees of accuracy and efficiency. In general, there exist 3 methods, which are as follows.

• Traditional facial recognition
• Biometric facial recognition
• 3D facial recognition

Traditional Facial Recognition

There are two methods to it. One is holistic facial recognition, in which an identifier’s complete face is analysed for identifying traits that match the target. Feature-based facial recognition, on the other hand, separates the relevant recognition data from the face before applying it to a template that is compared against prospective matches.

Detection – Facial recognition software detects the identifier’s face in an image
Analysis – Algorithms determine the unique facial biometrics and features, such as the distance between nose and mouth, size of eyelids, forehead, and other characteristics
Identification – The software can now compare the target faceprint to other faceprints in the database to find a match

Overview of Facial Recognition System

Biometric Facial Recognition

Skin and face biometrics are a growing topic in the field of facial recognition that has the potential to improve the accuracy of facial recognition technologies dramatically. A skin texture analysis examines a specific area of a subjects’ skin, using an algorithm to take very precise measurements of wrinkles, textures, and pores.

3D Facial Recognition

It’s a technique that uses the three-dimensional geometry of the human face to create a three-dimensional model of the facial surface. It employs specific aspects of the face to identify the subject, such as the curvature of the eye socket, nose, and chin, where hard tissue and bone are most visible. These regions are all distinct from one another and do not change throughout time. 3D face recognition can achieve more accuracy than its 2D counterpart by analysing the geometry of hard properties on the face. In the 3D facial recognition technology, sensors are employed to capture the shape of the face with more precision. Unlike standard facial recognition systems, 3D facial recognition is unaffected by light, and scans can even be done in complete darkness. Another advantage of 3D facial recognition is that it can recognize a target from many angles rather than just a straight-on appearance.

Applications of Facial Recognition Technology Retail

Face recognition in retail opens an ample number of possibilities for elevating the customer experience. Store owners can collect data about their customers’ visits (such as their reactions to specific products and services) and then conclude how to personalize their offerings. They can offer unique product packages to the clients based on their previous purchasing history and insights. Vending machines in Japan, for example, proposes drinks to customers based on their gender and age using facial recognition technology.

Healthcare

It has enhanced patient experience and reduced efforts for healthcare professionals by improving security and patient identification, as well as better patient monitoring and diagnosis. When a patient walks into the clinic, the facial recognition system scans their face and compares it to a database held by the hospital. Without the need for paperwork or other identification documents, the patient’s identity and health history are verified in real-time.

Security Companies

Nowadays machines that can effectively recognize individuals open a host of options for the security industry, the most important of which is the potential to detect illicit access to areas where non-authorized people are prohibited. Artificial intelligence-powered face recognition software can help spot suspicious behaviour, track down known offenders, and keep people safe in crowded locations.

Fleet Management Services

Facial recognition could be used in fleet management to give alerts to unauthorized personnel attempting to obtain access to vehicles, preventing theft. The fact that distraction is the major cause of accidents, which is due to the usage of electronic gadgets. When a driver’s eyes aren’t on the road, facial recognition technology may be designed to detect it. It may also be trained to detect eyes that indicate an intoxicated or tired driver, improving the safety of driver & fleet vehicles.

Benefits of Facial Recognition Technology

With constantly evolving capabilities, it will be fascinating to see where Machine Learning based Facial Recognition technology will reach over next decade. The amount and quality of image data required to train any facial recognition program are critical to its performance. Many examples are required, and each one necessitates a significant number of pictures to develop a thorough comprehension of the face.

At Softnautics we offer Machine Learning services to assist organizations in the development of futuristic AI solutions like facial recognition systems, Machine Learning/Deep Learning algorithms that compare facial features to several data sets using random and view-based features, utilizing complex mathematical representations and matching methods. We develop powerful Machine Learning models for feature analysis, neural networks, eigenfaces, and automatic face recognition. We provide Machine Learning services and solutions with expertise on edge platforms (TPU, RPi), NN compiler for the edge, Computer Vision, Machine Vision, tools like TensorFlow, TensorFlow Lite, Docker, GIT, AWS deepLens, Jetpack SDK, and many more targeted for domains like Automotive, Multimedia, Industrial IoT, Healthcare, Consumer, and Security-Surveillance.

Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.

Contact us at business@softnautics.com for any queries related to your solution or for consultancy.

 

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Deep learning is witnessing a growing history of success, however, the large/heavy models that must be run on a high-performance computing system are far from optimal. Artificial intelligence is already widely used in business applications. The computational demands of AI inference and training are increasing. As a result, a relatively new class of deep learning approaches known as quantized neural network models has emerged to address this disparity. Memory has been one of the biggest challenges for deep learning architectures. It was an evolution of the gaming industry that led to the rapid development of hardware leading to GPUs that enables 50 layer networks of today. Still, the hunger for memory by newer and powerful networks is now pushing for evolutions of Deep Learning model compression techniques to put a leash on this requirement, as AI is quickly moving towards edge devices to give near to real-time results for captured data. Model quantization is one such rapidly growing technology that has allowed deep learning models to be deployed on edge devices with less power, memory, and computational capacity than a full-fledged computer.

How did AI Migrate from Cloud to Edge?

A computer examines visual data and searches for a specified set of indicators, such as a person’s head shape, depth of their eyelids, etc. A database of facial markers is built, and an image of a face that matches the database’s essential threshold of resemblance suggests a possible match. Face recognition technologies, such as machine vision, modelling and reconstruction, and analytics, require the utilization of advanced algorithms in the areas of Machine Learning – Deep Learning and CNN (Convolutional Neural Network), which is growing at an exponential rate.

Edge AI mostly works in a decentralized fashion. Small clusters of computer devices now work together to drive decision-making rather than going to a large processing center. Edge computing boosts the device’s real-time response significantly. Another advantage of edge AI over cloud AI is the lower cost of operation, bandwidth, and connectivity. Now, this is not easy as it sounds. Running AI models on the edge devices while maintaining the inference time and high throughput is equally challenging. Model Quantization is the key to solving this problem.

The need for Quantization?

Now before going into quantization, let’s see why neural network in general takes so much memory.

Elements of ANN

As shown in the above figure a standard artificial neural network will consist of layers of interconnected neurons, with each having its weight, bias, and activation function. These weights and biases are referred to as the “parameters” of a neural network. This gets stored physically in memory by a neural network. 32-bit floating-point values are a standard representation for them allowing a high level of precision and accuracy for the neural network.

Getting this accuracy makes any neural network take up much memory. Imagine a neural network with millions of parameters and activations, getting stored as a 32-bit value, and the memory it will consume. For example, a 50-layer ResNet architecture will contain roughly 26 million weights and 16 million activations. So, using 32-bit floating-point values for both the weights and activations would make the entire architecture consume around 168 MB of storage. Quantization is the big terminology that includes different techniques to convert the input values from a large set to output values in a smaller set. The deep learning models that we use for inferencing are nothing but the matrix with complex and iterative mathematical operations which mostly include multiplications. Converting those 32-bit floating values to the 8 bits integer will lower the precision of the weights used.

Quantization Storage Format 

Due to this storage format, the footprint of the model in the memory gets reduced and it drastically improves the performance of models. In deep learning, weights, and biases are stored as 32-bit floating-point numbers. When the model is trained, it can be reduced to 8-bit integers which eventually reduces the model size. One can either reduce it to 16-bit floating points (2x size reduction) or 8-bit integers (4x size reduction). This will come with a trade-off in the accuracy of the model’s predictions. However, it has been empirically proven in many situations that a quantized model does not suffer from a significant decay or no decay at all in some scenarios.

Quantized Neural Network model 

How does the quantization process work?

There are 2 ways to do model quantization as explained below:

Post Training Quantization:

As the name suggests, Post Training Quantization is a process of converting a pre-trained model to a quantized model viz. converting the model parameters from 32-bit to 16-bit or 8-bit. It can further be of 2 types. One is Hybrid Quantization, where you just quantize weights and do not touch other parameters of the model. Another is Full Quantization, where you quantize both weights and parameters of the model.

Quantization Aware Training:

As the name suggests, here we quantize the model during the training time. Modifications are done to the network before initial training (using dummy quantize nodes) and it learns the 8-bit weights through training rather than going for conversion later.

Benefits and Drawbacks of Quantization

Quantized neural networks, in addition to improving performance, significantly improve power efficiency due to two factors: lower memory access costs and better computation efficiency. Lower-bit quantized data necessitates less data movement on both sides of the chip, reducing memory bandwidth and conserving a great deal of energy.

As mentioned earlier, it is proven empirically that quantized models don’t suffer from significant decay, still, there are times when quantization greatly reduces models’ accuracy. Hence, with a good application of post quantization or quantization-aware training, one can overcome this drop inaccuracy.

Model quantization is vital when it comes to developing and deploying AI models on edge devices that have low power, memory, and computing. It adds the intelligence to IoT eco-system smoothly.

At Softnautics, we provide AI and Machine Learning services and solutions with expertise on cloud platforms accelerators like Azure, AMD, edge platforms (TPU, RPi), NN compiler for the edge, and tools like Docker, GIT, AWS DeepLens, Jetpack SDK, TensorFlow, TensorFlow Lite, and many more targeted for domains like Multimedia, Industrial IoT, Automotive, Healthcare, Consumer, and Security-Surveillance. We can help businesses to build high-performance cloud-to-edge Machine Learning solutions like face/gesture recognition, human counting, key-phrase/voice command detection, object/lane detection, weapon detection, food classification, and more across various platforms.

Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.

Contact us at business@softnautics.com for any queries related to your solution or for consultancy.

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At the end of 2021, the artificial intelligence market was estimated to be a value of $58.3 billion. This figure is bound to increase and is estimated to grow tenfold over the next 5 years and reach $309.6 billion by 2026. Given such popularity of AI technology, companies extensively want to build and deploy solutions with AI applications for their businesses. In today’s technology-driven world AI has become an integral part of our life. As per a report by McKinsey, AI adoption is continuing its steady rise: 56% of all respondents report AI adoption in at least one business function, up from 50% in 2020. This increase in adoption is due to evolving strategies for building and deploying AI applications. Various strategies are evolving to build and deploy AI models. Container applications are one such strategy. MLOps are becoming increasingly stable. If you are unfamiliar with Machine Learning operations, then it is a collection of principles, practices, and technologies that help to increase the efficiency of machine learning workflows. It is based on DevOps, and just as DevOps has streamlined the SDLC from development to deployment, MLOps accomplish the same for machine learning applications. Containerization is one of the most intriguing and emerging technologies for developing and delivering AI applications. A container is a standard unit of software packaging that encapsulates code and all its dependencies in a single package, allowing programs to move from one computing environment to another rapidly and reliably. Docker is at the forefront of application containerization.

What Are Containers?

Containers are logical boxes that contain everything an application requires to execute. The operating system, application code, runtime, system tools, system libraries, binaries, and other components are all included in this software bundle. Optionally, some dependencies might be included or excluded based on the availability of specific hardware. These containers run directly within the host machine kernels. The container will share the host machine’s resources (like CPU, disks, memory, etc.) and eliminate the extra load of a hypervisor. This is the reason why containers are “lightweight “.

Why Are Containers So Popular?

• First, they are lightweight since the container shares the machine operating system kernels. It doesn’t need an entire operating system in place to run the application. VirtualBox, popularly known as VM’s, require installation of complete OS making them quite bulky.
• Containers are portable and can easily be transported from one machine to another machine with all the required dependencies within it. They enable developers and operators to improve CPU and memory utilization of physical machines.
• Among container technology, Docker is the most popular and widely used platform. Not only the Linux-powered Red Hat and Canonical have embraced Docker, but also companies like Microsoft, Amazon, and Oracle are relying on it. Today, almost all IT and cloud companies have adopted docker, and are widely used to provide their solution with all the dependencies.

Virtual Machines vs Containers

Is There Any Difference between Docker and Containers?

• Docker has widely become a synonym for containers because it is open-source, has a huge community base, and is a quite stable platform. But container technology isn’t new, it has been incorporated into Linux in the form of LXC for more than 10 years, and similar operating-system-level virtualization has also been offered by FreeBSD jails, AIX Workload Partitions, and Solaris Containers.
• Dockers can make the process easier by merging OS and package needs into a single package, which is one of the differences between containers and dockers.
• We’re often perplexed as to why docker is employed in the field of data science and artificial intelligence, yet it’s mostly used in DevOps. ML and AI, like DevOps, have inter-OS dependencies. As a result, a single code can run on Ubuntu, Windows, AWS, Azure, Google Cloud, ROS, a variety of edge devices, or anywhere else.

Container Application for AI / ML:

Like any software development, AI applications also face SDLC challenges when assembled and run by various developers in a team or in collaboration with multiple teams. Due to the constant iterative and experimental nature of AI applications, there comes a point where the dependencies might wind up crisscrossing, causing inconveniences for other dependent libraries in the same project.

To Explain:

 

The need for Container Application for AI / ML

The issues are true, and as a result, there is a requirement for acceptable documentation of each step to follow if you’re presenting a project that requires a specific method of execution. Imagine you have multiple python virtual environments for different models of the same projects, and without updated documentation, you may wonder what are these dependencies for? Why do I get conflicts while installing newer libraries or updated models etc.? Developers constantly face this dilemma “It works on my machine” and constantly try resolving it.

Why it’s working on my machine

Using Docker, all of this can be made easier and faster. Containerization can help you save a lot of time updating documents and make the development and deployment of your program go more smoothly in the long term. Even by pulling multiple images which will be platform-agnostic, we can serve multiple AI models using docker containers.

The application written fully on the Linux platform can be run on the Windows platform using docker, which can be installed on a Windows workstation, making code deployment across platforms much easier.

 

Deployment of code using docker container

Benefits of Converting entire AI application development to deployment pipeline into a container:

• Separate containers for each AI model for different versions of frameworks, OS, and edge devices/ platforms.
• Having a container for each AI model for customization of deployments. Ex: One container is developer-friendly while another is user-friendly and requires no coding to use.
• Individual containers for each AI model for different releases or environments in the AI project (development team, QA team, UAT (User Acceptance Testing), etc.)

Container applications truly accelerate the AI application development-deployment pipeline more efficiently and help maintain and manage multiple models for multiple purposes. Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions. Contact us at business@softnautics.com for any queries related to your solution or for consultancy. [elementor-template id=”12005″]

Machine Vision has exploded in popularity in recent years, particularly in the manufacturing industry. Companies can profit from the technology’s enhanced flexibility, decreased product faults, and improved overall production quality. The ability of a machine to acquire images, evaluate them, interpret (the situation), and then respond appropriately is known as Machine Vision. Smart cameras, image processing, and software are all part of the system. Vision technology can assist the manufacturing industry on many levels, thanks to significant advancements in imaging techniques, smart sensors, embedded vision, machine and supervised learning, robot interfaces, information transmission protocols, and image processing capabilities. By decreasing human error and ensuring quality checks on all goods traveling through the line, vision systems improve product quality. The Industrial Machine Vision market is valued at $53.38 billion by the end of 2028 and is expected to grow at a rate of 9.90% as per the reports stated by the Data Bridge Research group. Furthermore, an increase in the demand for inspection in the manufacturing units/factories with higher product quality measures, are likely to drive up demand for industrial Machine Vision under AI technologies and propel the market forward.

Applications of Machine Vision in Manufacturing

Predictive Maintenance
Manufacturing enterprises need to use a variety of large machinery to produce vast quantities of goods. To avoid equipment downtime, certain pieces of equipment must be monitored regularly. Examining each piece of equipment in a manufacturing facility by hand is not only time-consuming but also costly and gaffe. The idea was to only fix the equipment when it failed or became problematic. However, utilizing this technique to restore the equipment can have significant consequences for worker productivity, manufacturing quality, and cost. What if, on the other hand, manufacturing organizations could predict the state of their machinery’s operation and take proactive steps to prevent a breakdown from occurring? Let’s examine the situation where some production processes take place at high temperatures and in harsh environments, material deterioration and corrosion are prevalent. As a result, the equipment deforms. If not addressed promptly, this can lead to significant losses and the halting of the manufacturing process. Machine vision systems can monitor the equipment in real-time and predict maintenance based on multiple wireless sensors that provide data of a variety of parameters. If any variation from metrics indicates corrosion/over-heating, the vision systems can notify the appropriate supervisors, who can then take pre-emptive maintenance measures.

Goods Inspection

Manufacturing firms can use machine vision systems to detect faults, fissures, and other blemishes in physical products. Moreover, when the product is being built, these systems may easily check for accurate and reliable component or part dimensions. Images of goods will be captured by machine vision systems. The trained Machine Vision model will compare these photographs with acceptable data limit & will then pass or reject the goods. Any errors or flaws will be communicated via appropriate notification/alert. This is how manufacturers may use machine vision systems to do automatic product inspections and accurate quality control, resulting in increased customer satisfaction.

Scanning Barcodes

Manufacturers can automate the complete scanning process by equipping machine vision systems with enhanced capabilities such as Optical Character Recognition (OCR), Optical Barcode Recognition (OBR), Intelligent Character Recognition (ICR), etc. As in the case of OCR text contained in photographed labels, packaging, or documents can be retrieved and validated against databases. This way, products with inaccurate information can be automatically identified before they leave the factory, limiting the margin for error. This procedure can be used to apply information on drug packaging, beverage bottle labels, and food packaging information such as allergies or expiration dates.

Role of Machine Vision in Manufacturing

 

3D Vision System

A machine vision inspection system is used in a production line to perform tasks that humans find difficult. Here, the system creates a full 3D model of components and connector pins using high-resolution images.

As components pass through the manufacturing plant, the vision system captures images from various angles to generate a 3D model. When these images are combined and fed into AI algorithms, they detect any faulty threading or minor deviations from the design. This technology has a high level of credibility in manufacturing industries for automobiles, oil & gas, electronic circuits, and so on.

Vision-Based Die Cutting

The most widely used technologies for die-cutting in the manufacturing process are rotary and laser die-cutting. Hard tooling and steel blades are used in rotary, while high-speed laser light is used in laser. Although laser die cutting is more accurate, cutting tough materials is difficult, while rotary cutting can cut any material.

To cut any type of design, the manufacturing industry can use machine vision systems to do rotary die cutting that is as precise as laser cutting. After feeding the design pattern to the vision system, the system will direct the die cutting machine, whether laser or rotary, to execute accurate cutting.

As a result, Machine Vision with the help of AI and deep learning algorithms can transform the manufacturing industry’s efficiency and precision. Such models, when combined with controllers and robotics, can monitor everything that happens in the industrial supply chain, from assembly to logistics, with the least amount of human interaction. It eliminates the errors that come with manual procedures and allows manufacturers to focus on higher cognitive activities. As a result, Machine Vision has the potential to transform the way a manufacturing organization/unit does business.

At Softnautics, we help the manufacturing industry to design Vision-based ML solutions such as image classification & tagging, gauge meter reading, object tracking, identification, anomaly detection, predictive maintenance and analysis, and more. Our team of experts has experience in developing vision solutions based on Optical Character Recognition, NLP, Text Analytics, Cognitive Computing, etc.

Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.

Contact us at business@softnautics.com for any queries related to your solution or for consultancy.

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The concept of autonomous vehicles is now becoming a reality with the advancement of Computer Vision technologies. Computer Vision helps in the areas of perception building, localization and mapping, path planning, and making effective use of controllers to actuate the vehicle. The primary aspect is to understand the environment and perceive it by using the camera to identify other vehicles, pedestrians, roads, pathways and with the use of sensors such as Radar, LIDAR, complement those data obtained by the camera.

Histogram of oriented Gradients (HOG) and classifiers for object detection got a lot of attention with the use of machine learning. Classifiers train a model for the identification of shape by examining its different gradients and HOG retains the shapes and directions of each pixel. A typical vision system consists of near and far radars, front, side, and rear cameras with ultrasonic sensors. This system assists in safety-enabled autopilot driving and retains data that can be useful for future purposes.

The Computer Vision market size stands at $9.45 billion and is expected to reach $41.1 billion by 2030 as per the report by Allied market research. Global demand for autonomous vehicles is growing. It is expected that by 2030, nearly 12% to 17% of total vehicle sales will belong to the autonomous vehicle segment. OEMs across the globe are seizing this opportunity and making huge investments in ADAS, Computer Vision, and connected car systems.

Computer Vision with Sensor

How does Computer Vision enable Autonomous Vehicles?

Object Detection and Classification

It helps in identifying both stationary as well as moving objects on the road like vehicles, traffic lights, pedestrians, and more. For the avoidance of collisions, while driving, the vehicles continuously need to identify various objects. Computer Vision uses sensors and cameras to collect entire views and make 3D maps. This makes it easy for object identification, avoiding collision, and makes it safe for passengers.

Information Gathering for Training Algorithms

Computer Vision technology makes use of cameras and sensors to gather large sets of data inducing type of location, traffic and road conditions, number of people, and more. This helps in quick decision-making and assists autonomous vehicles to make use of situational awareness. This data can be further used in training the deep learning model to enhance performance.

Low-Light Mode with Computer Vision

The complexity of driving in a low light mode is much different than driving in daylight mode as images captured in a low light mode are often blurry and unclear which makes driving unsafe. With Computer Vision vehicles can detect low light conditions and make use of LIDAR sensors, HDR sensors, and thermal cameras to create high-quality images and videos. This improves safety for night driving.

Vehicle Tracking and Lane Detection

Cutting lanes can become a daunting task in the case of autonomous vehicles. Computer Vision with assistance from deep learning can use segmentation techniques to identify lanes on-road and continue in the stipulated lane. For tracking and understanding behavioral patterns of a vehicle, Computer Vision uses bounding box algorithms to assess its position.

Assisted Parking

The development in deep learning with convolutional neural networks (CNN) has drastically improved the accuracy level of object detection. With the help of outward-facing cameras, 3D reconstruction, parking slot marking recognition makes it is quite easy for autonomous vehicles to park in congested spaces, thereby eliminating wastage of time and effort. Also, IoT-enabled smart parking systems determine the occupancy of the parking lot and send a notification to the connected vehicles nearby.

Insights to Driver Behaviour

With the use of inward-facing cameras, Computer Vision can monitor driver’s gestures, eye movement, drowsiness, speedometer, phone usage, etc. which have a direct impact on road accidents and passengers’ safety. Monitoring all the parameters and giving timely alerts to drivers, avoids fatal road incidents and augments safety. Especially in the case of logistics & fleet companies, the vision system can identify and provide real-time data for the improvement of driver performance for maximizing their business.

The application of vision solutions into automotive is gaining immense popularity. With the inception of deep learning algorithms such as route planning, object detection, and decision making driven by powerful GPUs along with technologies ranging from SAR/thermal camera hardware, LIDR & HDR sensors, radars, it is becoming simpler to execute the concept of autonomous driving.

At Softnautics, we help automotive businesses to design Computer Vision-based solutions such as automatic parallel parking, traffic sign recognition, object/lane detection, driver attention system, etc. involving FPGAs, CPUs, and Microcontrollers. Our team of experts has experience working with autonomous driving platforms, functions, middleware, and compliances like adaptive AUTOSAR, FuSa (ISO 26262), and MISRA C. We support our clients in the entire journey of intelligent automotive solution design.

Read our success stories related to Machine Learning expertise to know more about our services for accelerated AI solutions.

Contact us at business@softnautics.com for any queries related to your solution or for consultancy.

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Artificial Intelligence (AI) is one of the emerging technologies that try to simulate human reasoning in AI systems. Researchers have made significant strides in weak AI systems, while they have only made a marginal mark in robust AI systems.

AI is even contributing to the development of a brain-controlled robotic arm that can help a paralyzed person feel again through complex direct human-brain interfaces. These new AI-enabled systems are revolutionizing from commerce and healthcare to transportation and cybersecurity.

This technology has the potential to impact nearly all aspects of our society, including our economy; still the development and use of the new technologies it brings are not without technical challenges and risks. AI must be developed dependably to ensure reliability, security, and accuracy.