Edge Intelligent Visual Monitoring of Substations Based on Lightweight Object Detection Model

Edge intelligent visual monitoring of substations performs intelligent detection on collected images at edge intelligent perception terminals, representing a novel substation intelligent monitoring paradigm distinct from the cloud computing mode. However, edge computing devices have limited computing power and memory. To enhance the efficiency and feasibility of edge-side object detection algorithms, this paper proposes a lightweight detection algorithm for edge intelligent inspection of substations. Firstly, YOLOv6 is adopted as the baseline model and QARepVGG is introduced to yield QAYOLOv6, making the model conducive to quantization. Secondly, group channel pruning (GCP) and efficient layer pruning (ELP) are proposed to respectively prune the channels and layers of the model, reducing redundant parameters. Finally, quantization-aware training (QAT) is employed to quantize the model to INT8, further reducing computational cost and boosting detection speed. Knowledge distillation is utilized in model training and fine-tuning processes to enhance model accuracy. Experimental results show that on the visible light defect dataset of substations, we achieve a reduction in model computational cost by 63% through pruning and attain over a fivefold acceleration of the model on edge platforms after INT8 quantization, with minimal loss in accuracy.