基于YOLO模型的作业环境实时反光背心检测

doi:10.12066/j.issn.1007-2861.2686

Abstract

Abstract: Based on the YOLOv7 model, the YOLOv7-globel attention mechanism (YOLO-GAM) model was proposed to enhance the model's focus on critical regions. Additionally, a multi-scale training scheme was introduced to improve the model's ability to detect small targets, and a two-stage enhanced detection algorithm was designed, which effectively mitigated the degradation of detection performance caused by occlusion, overlapping, and small targets. With an input image resolution of $640\times 640$, the scheme's detection speed could meet the real-time requirements of the actual production environment and outperform the related algorithms in terms of performance.

Key words: target detection, reflective vest, deep learning, YOLO

CLC Number:

TP391.4

ZHU Shuo, WANG Yongfang, LI Zixuan, CHEN Wei. Real-time reflective vest detection in operational environments based on YOLO[J]. Journal of Shanghai University（Natural Science Edition）, 2025, 31(4): 691-703.

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Real-time reflective vest detection in operational environments based on YOLO

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