中圖分類號：TP273 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.256317
中文引用格式： 王曉旋，李博，鐘鵬. 基于邊緣計(jì)算的智慧工地視頻分析系統(tǒng)設(shè)計(jì)[J]. 電子技術(shù)應(yīng)用，2025，51(7)：52-59.
英文引用格式： Wang Xiaoxuan，Li Bo，Zhong Peng. Design of an edge-computing-based intelligent construction site video analysis system[J]. Application of Electronic Technique，2025，51(7)：52-59.

Design of an edge-computing-based intelligent construction site video analysis system

Abstract： To accurately identify workers' failure to wear safety helmets correctly and enable rapid system response, this paper designs an intelligent construction site video analysis system deployed at the edge. The hardware architecture employs a cascaded configuration of two AX650N chips, significantly enhancing the system's computational power. The software solution is based on the Ubuntu operating system and integrates the state-of-the-art object detection algorithm YOLOv8n. By replacing pooling layers in the SPPF module with dilated convolutions sharing convolutional layers, the detection speed and accuracy are improved. Additionally, the WIoU loss function with dynamic non-monotonic modulation is introduced to enhance detection capabilities for low-quality samples. Experimental results demonstrate that the optimized algorithm model achieves a 1.8% improvement in average detection precision, a 17.4% reduction in model weight space, and a 7.3% increase in detection speed. System testing in multiple real-world scenarios shows that the intelligent analysis of video target behaviors achieves a success rate of over 97% with an average response time of less than 1 second. This system significantly enhances construction site management efficiency and safety.

Key words : edge computing；intelligent site；video analysis；cascade；YOLO