Lightweight target detection algorithm based on partial convolution

Abstract

You only look once (YOLO) target detection algorithms have garnered substantial attention in engineering inspection tasks due to their remarkable accuracy and swift detection speed. However, adapting the YOLOv7 algorithm to engineering construction presents challenges in balancing lightweight design and high precision. This work introduces a YOLOv7 lightweight detection algorithm, leveraging partial convolution to efficiently reduce network parameters without compromising detection speed. Integrating partial convolution into the network's backbone is a highly effective parameter reduction strategy. Additionally, we propose the SE_Conv module following quantitative analyses through experiments, thereby significantly enhancing the algorithm's detection accuracy. To address the impact of low-quality data on detection effectiveness in engineering tasks, the advanced bounding box loss function WIoU is employed. In a practical application scenario involving helmet detection, the algorithm achieves a notable 23.4% reduction in parameters and a substantial 28.2% decrease in operations through partial convolution. Subsequent experiments with the SE_Conv module yield a noteworthy 1.4% improvement in results. Significantly, in head detection, our algorithm surpasses YOLOv8 algorithm by 2.6% and single shot multibox detector algorithm by 9.4%. These findings underscore the efficacy of our proposed algorithm in engineering applications.