AHLNet: Adaptive Multihead Structure and Lightweight Feature Pyramid Network for Detection of Live Working in Substations

With the increasing demand for power in society, there is much live equipment in substations, and the safety and standardization of live working of workers are facing challenges. Aiming at these problems of scene complexity and object diversity in the real-time detection of the live working safety of substation workers, an adaptive multihead structure and lightweight feature pyramid-based network (AHLNet) is proposed in this study, which is based on YOLOV3. First, we take AH-Darknet53 as the backbone network of YOLOV3, which can introduce an adaptive multihead (AMH) structure, reduce the number of network parameters, and improve the feature extraction ability of the backbone network. Second, to reduce the number of convolution layers of the deeper feature map, a lightweight feature pyramid network (LFPN) is proposed, which can perform feature fusion in advance to alleviate the problem of feature imbalance and gradient disappearance. Finally, the proposed AHLNet is evaluated on the datasets of 16 categories of substation safety operation scenarios, and the average prediction accuracy MAP₅₀ reaches 82.10%. Compared with YOLOV3, MAP₅₀ is increased by 2.43%, and the number of parameters is 90 M, which is only 38% of the number of parameters of YOLOV3. In addition, the detection speed is basically the same as that of YOLOV3, which can meet the real-time and accurate detection requirements for the safe operation of substation staff.