Computer vision technology is based on the automated analysis of visual data. Following an interdisciplinary approach, it combines Artificial Intelligence, image processing, and computer science to enable machines to acquire, interpret, and understand images and videos. This technology has evolved a lot in recent years, driven above all by the growing computing power and the availability of large datasets.
Technical limitations in computer vision technology
Despite the opportunities and interest, implementing computer vision systems in embedded devices, such as industrial control systems, robotics, drones, or IoT devices, introduces some complex challenges. First, the limited computational and memory hardware capabilities of embedded devices require careful optimization of computer vision algorithms. Deep neural networks, while highly effective, can also be very expensive in terms of power and memory.
Another aspect to consider is energy efficiency: many embedded systems, such as those used in drones or remote sensors, operate on battery power, so in these cases, it is essential to minimize processor power consumption. Added to this, is the robustness of vision systems, especially in uncontrolled environments. While deep learning models have demonstrated outstanding performance in well-defined contexts and with high-quality datasets, they can be susceptible to sudden changes in environmental conditions, such as changes in lighting, camera angles, or noise, which is particularly problematic in embedded systems used in industrial or outdoor scenarios, where environmental conditions can vary dramatically.
Computer vision-based surveillance devices also raise concerns about the misuse of facial recognition technologies or the invasiveness of visual data collection. It is therefore essential that computer vision system designers incorporate measures to ensure the protection of personal data in compliance with privacy regulations.
Despite the opportunities and interest, implementing computer vision systems in embedded devices, such as industrial control systems, robotics, drones, or IoT devices, introduces some complex challenges. First, the limited computational and memory hardware capabilities of embedded devices require careful optimization of computer vision algorithms. Deep neural networks, while highly effective, can also be very expensive in terms of power and memory.
Another aspect to consider is energy efficiency: many embedded systems, such as those used in drones or remote sensors, operate on battery power, so in these cases, it is essential to minimize processor power consumption. Added to this, is the robustness of vision systems, especially in uncontrolled environments. While deep learning models have demonstrated outstanding performance in well-defined contexts and with high-quality datasets, they can be susceptible to sudden changes in environmental conditions, such as changes in lighting, camera angles, or noise, which is particularly problematic in embedded systems used in industrial or outdoor scenarios, where environmental conditions can vary dramatically.
Computer vision-based surveillance devices also raise concerns about the misuse of facial recognition technologies or the invasiveness of visual data collection. It is therefore essential that computer vision system designers incorporate measures to ensure the protection of personal data in compliance with privacy regulations.
Applications and solutions for computer vision
Despite some technical limitations, as mentioned above, the opportunities offered by computer vision are immense. The manufacturing sector is one of the biggest beneficiaries of this technology, where computer vision is used for quality control, process automation, and predictive maintenance. Systems can detect defects in products or anomalies in machinery with greater precision than humans, reducing costs and improving efficiency.
In the healthcare sector, computer vision is transforming medical care, with applications ranging from automated diagnosis of medical images to real-time patient monitoring using video cameras. The automotive industry is also exploiting the potential of computer vision, especially in the development of autonomous vehicles, where computer vision allows vehicles to “see” their surroundings, and recognize obstacles, road signs, and pedestrians.
Analog Devices provides a broad range of computer vision products and solutions specifically designed to support advanced machine vision applications. The products cover various aspects of image processing and accelerate the development of intelligent machine vision systems. With a comprehensive portfolio of advanced technologies, Analog Devices is today a key player in the machine vision market, with integrated and scalable solutions for numerous applications such as industrial and automation, advanced robotics, automotive and autonomous driving, healthcare (medical imaging, telemedicine, diagnostic image analysis), security and consumer.
The company’s key products include integrated solutions for LiDAR and Radar systems, designed for computer vision applications in autonomous vehicles, and ADAS systems that combine different technologies to improve the perception of the surrounding environment, providing detailed, three-dimensional images. There is a growing demand for ADAS solutions to combine efficient power management in smaller footprints, combined with high-speed connectivity, complex interconnections, and data integrity.
Advanced Driver Assistance Systems (ADAS) include technologies designed to assist drivers while driving, improving vehicle safety and efficiency. ADAS features can include obstacle and pedestrian detection, adaptive cruise control, traffic sign recognition, lane keeping, blind spot monitoring, and automatic emergency braking. ADAS uses sensors, cameras, radar, and LiDAR to collect data about the surrounding environment and assist the driver in making correct and safe decisions. In this area, Analog Devices’ radar sensors are particularly appreciated for their accuracy in detecting moving objects.
ADI’s next-generation ADAS architectures combine AI and machine learning with computer vision to improve object recognition, scene understanding, and video analytics, while also enabling faster time to market. ADAS systems, including precision sensing, intelligent power management, high-speed connectivity, and data integrity, enable efficient design with a small footprint of external components. All these ADAS capabilities are enabled by a set of sensors distributed throughout the car, networked to I/O modules, actuators, and controllers. Driver monitoring systems, parking and autonomous vehicle cameras, acoustic warning systems for electric vehicles, and emergency vehicle detection complete the portfolio.
The flexibility and scalability of next-generation ADAS systems aim to enable efficient and precise operations, reduce design complexity, and accelerate development time. ADI provides precision sensing, intelligent power management, and connectivity, which support sensor fusion and processing from cameras, radar, and LIDAR systems.
ADI also provides image sensors optimized for capturing high-resolution images, with applications ranging from machine vision to video acquisition. The sensors support capabilities such as low-light image processing and high dynamic range and are widely used in:Industrial automation and robotics
Medical imaging devices
ADAS and autonomous vehicles
There are also advanced processing platforms with low-power embedded vision capabilities and hardware accelerators for image processing. All Analog Devices embedded vision solutions offer a combination of advanced sensors and high-performance processing hardware, suitable for industrial, automotive, and healthcare contexts.
For example, the range of products such as the Blackfin Embedded Vision Processor, is designed to provide optimized processing power for vision applications. ADI provides processors and processing solutions to handle the data flow from image sensors, accelerating the process of inference and visual analysis; these include digital signal processors (DSPs) optimized for machine vision and deep learning.
The Blackfin ADSP-BF609 processor is optimized for embedded vision and video analytics applications using a dual-core fixed-point DSP processor with a unique pipelined vision processor (PVP). The PVP is a set of functional blocks alongside the Blackfin cores designed to accelerate image processing algorithms and reduce overall bandwidth requirements. Other processor specifications include an advanced high-performance infrastructure, large on-chip memory, and a feature-rich peripheral set with extensive connectivity options. The ADSP-BF609 processor is ideal for many embedded vision applications such as automotive advanced driver assistance systems (ADAS), machine vision and robotics for manufacturing, security and surveillance analytics, and barcode scanners.
The ADSD3500 is a time-of-flight (ToF) depth image signal processor for Analog Devices ToF products such as the ADTF3175 and ADSD3030. The ADSD3500 supports full depth, active brightness, and confidence calculation for 640×480 resolution and partial depth calculation (pre-phase unwrap) for 1024×1024 resolution. The data flow and processing are controlled via the integrated ARM Cortex-M33. The calculation is performed using dedicated hardware and memory, enabling a low-power ToF depth ISP solution.
The ADSD3500 also controls the booting of the image sensor module, loading of calibration data, and triggering of frames. Designed for an operating temperature range of -25°C to +85°C, it addresses the following application fields: augmented reality (AR) systems, robotics, building automation, and machine vision systems. The ADSD3500 is available in a 3.47mm x 3.47mm WLCSP package.
ADI also provides a range of high-quality video acquisition and transmission solutions, including high-speed video interfaces, encoders, decoders, and transceivers.
Computer vision on low-cost platforms
Object detection is one of the main applications of Artificial Intelligence, which is used both at the Machine Learning and Deep Learning levels. The well-known single-board computer brand, Raspberry Pi, has had a significant impact on the field of embedded computer vision. Thanks to its compact and powerful boards, Raspberry Pi can run artificial vision algorithms even on low-cost devices, such as the Pi Camera module, which integrates perfectly with the platform for embedded vision projects, making it today the preferred tool for hobbyists, academic researchers and developers of prototypes and real applications.
For example, it is possible to implement a real-time automatic object detection and identification application on Raspberry Pi through TensorFlow, an open-source platform for Machine Learning designed to facilitate the construction, training, and deployment of Machine Learning and Artificial Intelligence models. To do this, all you need is a common Raspberry Pi 3, a camera for image acquisition, and an SD memory card.
Additionally, the neural network can be trained to detect specific classes of objects within the same image, turning the Raspberry Pi into a highly customized detection system for computing applications. Even a low-cost embedded platform with performance that cannot match specialized AI hardware can run an object recognition model with acceptable results. With its versatility and the support of a large development community, the Raspberry Pi provides a solid foundation for embedded vision applications, allowing you to integrate cameras, sensors, and hardware accelerators into your designs.
Conclusions and Development Prospects
The field of computer vision represents today one of the most dynamic frontiers of modern technology. Designers of computer vision systems must achieve a good compromise between the management of computational resources for image and video processing, robustness of algorithms, and precision of expected results, without losing sight of energy savings. Thanks to innovations by companies in the sector, the development and implementation of powerful hardware platforms are now more accessible and open the doors to new sectors and increasingly intelligent and performing solutions, even in advanced applications and extreme conditions.
Website: International Research Awards on Computer Vision
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