NEC Corporation has developed a “gradual deep learning-based object detection technology” that enables efficient and high throughput object detection for video analytics while maintaining detection accuracy.
This technology enables up to eight times the processing speed of object detection for large volumes of images, even on edge devices with limited processing capacity.
NEC aims to commercialize this technology in fiscal 2022, following further research and development.
Video analytics are expected to be utilized for a wide range of applications, such as analyzing camera images of vehicles at intersections, optimizing traffic control, and analyzing camera images of stores and warehouses to detect an intrusion or to optimize facility management.
To perform these video analytics in real-time, it is ideal to process them on an edge device near a sensor, such as a camera.
However, because cooling is difficult to manage and electricity consumption is restricted in edge devices, high-performance processors such as GPUs used in high-performance servers are not available, and processing capacity is constrained.
In video analytics, object detection software that utilizes deep learning (hereinafter “object detection AI model”) performs object detection processing to find the object to be analyzed from images captured by a camera
Since highly accurate object detection AI models have a large number of operations though, it is difficult for edge devices to process a large number of images due to constraints on processing capacity.
If the number of operations for a high-speed object detection AI model is reduced, for example, the accuracy declines and the recognition accuracy requirements for image analysis cannot be met.
Going forward, NEC aims to enhance the safety, security, and convenience of society by expanding the use of video analytics technologies to a wide variety of applications.