A Multiscale Grouped Convolution and Lightweight Adaptive Downsampling-Based Detection of Protective Equipment for Power Workers

Abstract

Convolutional neural network-based detection models have been extensively applied in industrial production for monitoring the use of safety protection equipment, ensuring worker safety. This paper addresses scenarios in electrical operations where the safety protection requirements are more comprehensive and stringent. This paper proposes an improved detection model, ML-YOLOv8n-Light, based on YOLOv8n, targeting issues of low detection efficiency and large model size that make deployment challenging in current safety protection equipment-wearing detection models for electrical operations. Our model confronts the disparity in safety protection equipment sizes with a novel, lightweight multi-scale grouped convolution (MSGC) scheme integrated into the architecture. A lightweight adaptive weight downsampling (LAWD) mechanism is also fashioned to replace the traditional downsampling methods, optimizing resource consumption without sacrificing performance. Additionally, to enhance the detection fidelity of smaller items, such as insulated gloves, we added feature-rich shallow maps and a dedicated detection head for such objects. To enhance the detection efficiency of YOLOv8n, inspired by part convolution, we improved the spatial pyramid pooling fast (SPPF) and the detection heads. The experiments conducted on the custom dataset power safe attire dataset (PSAD) showed that compared to the original model, mAP50 increased by 2.1%, mAP50-95 by 3.1%, with a 29% reduction in parameters, an 18% reduction in computations, and a 23% compression of the model size. There are fewer detection omissions at long distances and under occlusion, fewer false positives, and computing resources are allocated more efficiently.