Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 250100047-7.doi: 10.11896/jsjkx.250100047

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

Real-time Helmet Detection Algorithm for Roadway Engineering Construction Based on UAV Visual Inspection

WEN Ming1, WU Xingtang2, SHANG Yuhao2, ZHEN Jian3, YU Fucai1   

  1. 1 Beijing Academy of Emergency Management Science and Technology,Beijing 101101,China
    2 School of Control and Computer Engineering,North China Electric Power University,Beijing 102206,China
    3 Yangtze Delta Institute of Infrastructure,Hangzhou 310005,China
  • Online:2025-11-15 Published:2025-11-10
  • Supported by:
    Beijing Natural Science Foundation of China(9244040).

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Abstract: To ensure the safety of highway engineering construction personnel and reduce safety risks during the construction process,real-time detection of helmet usage has become a critical safety supervision method.Highway projects are characterized by numerous,long,and wide construction sites,often involving complex terrains such as mountain ranges and rivers.Traditional fixed-camera coverage has limitations and high costs.Drones,as flexible,low-cost,and highly visible image acquisition tools,can effectively address these challenges,especially in high-risk areas that are difficult to cover with traditional methods.This paper proposes a real-time helmet detection algorithm based on an improved eXtended Difference of Gaussians(XDOG) and YOLOv5,aiming to solve the issues of misdetection and missed detection under variable lighting conditions,scale,and shape changes in images captured by drones.In complex construction environments,the features of safety helmets are often hard to distinguish from backgrounds or other objects.The XDOG module is introduced to enhance edge information in images,thereby highlighting the structural and detailed features of helmets to be detected.The difference-of-Gaussians results are further normalized and non-linearly activated to eliminate the effects of lighting variation and noise interference in construction environments.To ensure compatibility with the YOLOv5 network,the algorithm uses a 1×1 convolution layer to adjust the number of channels in the enhanced feature maps,and a residual connection is used to fuse the enhanced feature maps with the input image,thereby improving the robustness and accuracy of the network.Experimental results show that compared to traditional YOLOv5 and YOLOx models,the XDOG-YOLOv5 significantly improves detection accuracy,with notable gains in mAP@50 and mAP@50-95,demonstrating its effectiveness in real-time helmet detection for construction personnel.

Key words: Safety helmet detection, YOLOv5, Difference of Gaussians, Roadway engineering, Unmanned aerial vehicle

CLC Number: 

  • TP391
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