Digital transformation has brought a wave in the pandemic of 2020, and this transformation actually happened due to the rapid adaptation by the industries. It is indeed a 5 years transformation within 6 months! Not to mention, this accelerated adoption will happen to continue in 2021 also, and we will witness rapid progress in the areas of AI and industrial automation.
We have seen an aggravated application of computer vision in this context, which has played a key role in risk mitigation and management, automated security and monitoring, and helped to gain operational efficiency in industrial environments. This intervention of computer vision trends will dominate the year 2021 too.
Let see what the promising computer vision trends in 2021 are!
Related post – Computer Vision- AI for an eye- An overview
Computer Vision Trend #1: To Ensure Safety in the Workplace and people
Computer Vision Trend #2: Automatic Anomaly Detections for ensuring Quality
Computer vision technology has been adopted for PCB (produced printed circuit boards) production monitoring and detection. Such high-quality image data of PCB can check for 20+ anomalies and defects. This type of visual inspection will continue as a trend in other industries like Food and Beverages, Steel, Automotive, to automate and optimize visual inspections.
Computer Vision Trend #3: Thermal Imaging Analysis
COVID-19 has enforced a reduced workforce leading to declining margins in 2020. Hence, 2021 will be a crucial year to compensate for this gap. To do that, industrial leaders are looking for optimal Quality, accuracy, low cost, and flexibility by leveraging technologies like AI and computer vision. Non-destructive testing computer vision is one such application solution that can detect defects and identify the area with a high probability of anomalies using NDT techniques with radiology images. This automated computer vision feature widens the visible spectrum and detects any defects on the metal surface, which is usually invisible to the human eye.
Computer Vision Trend #4: Real-time application of Edge Computing
Edge computing allows data to be processed and analyzed at the collection point. Hence, it is related to physical machines. This highly efficient technology works wonders specifically for industries that are prone to a network outage. As the advancements of edge computing solve latency and network accessibility problems, this works as a boon for computer vision to respond in real-time. Furthermore, this helps to move only relevant insights to the cloud.
For example, in labor-intensive processes, software deployed on edge automates cycle time monitoring, whereas the edge box connects to multiple video management systems. These videos can detect anomalies near real-time while recording cycle time data across the entire operation to get a holistic picture of overall productivity. Here video analytics can verify the manufacturing, which saves hours of human labor and identify bottlenecks in real-time.
In 2021, video analytics software on edge will provide a viable option to business needs; faster computation, real-time insights, and high data security.
Computer Vision Trend #5: Helping hands through Sensor Data
Intuitive control interfaces are helping to improve the integration of sensor and vision data with new advancements. Here robust edge computing and closed-loop information exchange is also playing a crucial role. Automated surveillance cases have been unleashed through video analytics, which can automatically detect and alert security events, which undoubtedly contributes to physical security at national borders.
Computer Vision Trend #6: Leveraging Closed Loop Solutions
Before discussing the closed-loop solution, we need to know what the closed-loop control system is? Well, this is a system in which performing action depends on the system generated output. Usually, such systems are implemented with IIoT and ML for data analytics. However, implementing it with computer vision is a real challenge because it needs reliability and model accuracy.
Facial recognition is one of the applications of computer vision-based closed-loop solution. Another application is autonomous cars and unmanned vehicles. This vision system-based control vehicle movement in real-time using only visual feedback. In 2021, the computer vision trend will explore many closed-loop vision systems, which will provide many industrial use cases that go far beyond detecting and recommending. While it will optimize the process, the system will control process parameters without interaction with the operator.
Computer Vision Trend #7: More training on Auto-annotation
The sophistication of computer vision models depends on their training data volume and its Quality. Data annotation, a manual task, and can be sourced or outsourced is a tedious and complicated task too. This needs a good amount of training as well as knowledge of annotation tools. Besides, it would help if you tracked annotation and its speed. As a consequence, it increases the chance of human error and cost. However, with the advancement of AI, new computer vision platforms are in place that helps to automate data labeling. This also ensures faster data throughput and minimal error.
Computer vision trends 2021 will witness end-end automated solutions for image and video annotation. This automated solution will seamlessly fuel data pipelines helping faster activation of computer vision applications.
Computer Vision Trend #8: Solution for SAAS Video Analytics
A hardware upgrade is a usual bottleneck which creates a roadblock in implementing industry-wide video analytics solution. This cost a ton in a conventional surveillance system. However, there is a sharp surge in video analytics software used to integrate with existing infrastructure to get the insights. Over 300 parameters are used to train the software, which can track the minimal false alarm rate.
2021 can expect advancements in computer vision-based software for dynamically changing any camera to AI model mapping at any time.
Computer Vision Trend #9: More research on explanations of AI solutions
The computer vision model applies from simple object detection to event tracking with almost 99% accuracy. However, understanding how it works is essentially necessary, which is still in the research phase.
In an experiment by students at the University of Washington, they trained one computer vision-based neural network to differentiate between dogs and wolves with a specific dataset. Surprisingly, the model didn’t learn the differences between wolves and dogs but instead learned dogs were on grass in their picture and that wolves were on snow. To fulfill this requirement, there are novel AI-models explaining image classification or segmentation. These computer vision and machine learning models provide the reasoning behind the predictions made without subjecting them to human interpretation.
We’ll see more researchers develop tools and frameworks to understand and interpret the computer vision model behavior and performance in upcoming years.
Final Verdict
Computer vision is changing industries making smart lives more straightforward as well as fascinating. As a field, computer vision has got a lot of exposure and a good amount of investment worldwide. For example, the North American market has seen an investment of $120 million, while the Chinese market flooded to $3.9 billion for computer vision.
Keeping the growth prospects in mind, no doubt, advances or amalgamation of computer vision with different technologies will dominate the year 2021.