PG-YOLO: A Novel Lightweight Object Detection Method for Edge Devices in Industrial Internet of Things

Abstract

With the rapid development of Industrial Internet of Things (IIoT) technology, video surveillance devices and video data in IIoT environments are massively growing and increasingly important. Deploying rapidly evolving deep learning-based object detection algorithms in IIoT can improve the efficiency of video data utilization and increase the automation and intelligence of the IIoT. Facing the transmission latency problem of massive video data, the algorithms are better deployed in edge devices. However, due to the large size and high computing power requirements of existing object detection algorithms, it is difficult to deploy them on edge devices with limited performance. Therefore, to solve this problem, we propose a smaller and less computationally intensive object detection algorithm, PG-YOLO. We replaced all the convolutional modules of YOLOV5 with low computational ghost modules and streamlined the backbone network to improve the network performance. Then, we propose a better pruning algorithm to compress the network, and finally improve the accuracy by distillation to obtain PG-YOLO. PG-YOLO is smaller and faster and can be deployed in edge devices. In addition, we conducted experimental validation on the SHWD dataset. The experimental results show that PG-YOLO can compress the volume of the model by 9 times compared with YOLOv5s, and the compressed detection accuracy reaches 0.934, with only 0.1% loss of accuracy. Also, the time required for inference is reduced by 32.7%, and the inference speed is improved by 10 FPS. Compared with other object detection algorithms, PG-YOLO also has advantages.