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DSR-YOLOv8: A Dangerous Behavior Detection Algorithm for Electric Power Construction Workers Based on Depthwise Separable Residual Improved YOLOv8
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Author(s): Lingwen Meng (Electric Power Research Institute of Guizhou Power Grid Co. Ltd., China), Shasha Luo (Electric Power Research Institute of Guizhou Power Grid Co. Ltd., China), Jiangang Liu (Electric Power Research Institute of Guizhou Power Grid Co. Ltd., China), Bangming Zhang (Electric Power Research Institute of Guizhou Power Grid Co. Ltd., China)and Zhonghai Ruan (GuangZhou Power Electrical Technology Co., Ltd., China)
Copyright: 2026
Volume: 17
Issue: 1
Pages: 15
Source title:
International Journal of Ambient Computing and Intelligence (IJACI)
Editor(s)-in-Chief: Nilanjan Dey (Techno International New Town, Kolkata, India)
DOI: 10.4018/IJACI.404000
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Abstract
The power construction industry's growth demands efficient monitoring of high-risk worker behaviors, yet traditional methods are inefficient and existing models face false alarms in complex scenes. This study proposes DSR-YOLOv8, an improved YOLOv8 algorithm integrating three modules: (1) DSRAB using deep separable convolution and global pooling to enhance subtle action features and denoising; (2) SD_SPPF with multi-scale dilated kernels to expand the receptive field while reducing computational costs; (3) dynamic region-processing with partial convolutional heads to focus on critical areas and suppress interference. Evaluated on a self-built Dangerous Behavior Dataset (DBD) containing “helmet-wearing,” “no helmet,” and “smoking” scenarios, DSR-YOLOv8 achieved 91.2% accuracy (+3.5%) and 89.7% mAP (+3.6%) over baselines, demonstrating efficient hazardous behavior detection for enhanced safety in power construction.
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