提升工地安全帽检测精度，尤其改善小目标和遮挡情况下的识别效果。
Dynamic Attention and Bi-directional Fusion for Safety Helmet Wearing Detection
- 动态注意力机制融合尺度、空间与通道信息，增强多尺度感知能力。
- 双向融合策略使特征融合更精准，小目标检测性能提升1.7% mAP@[.5:.95]。
- 模型轻量化设计减少11.9% GFLOPs，适合部署在真实工地监控系统中。
保障施工现场安全需实时准确检测工人是否佩戴安全帽，但复杂环境、人群密集及建筑遮挡导致小目标或重叠目标难以识别。本文提出一种新型安全帽佩戴检测算法，引入检测头内的动态注意力机制，融合特征级注意力（尺度自适应）、空间注意力（定位）与通道注意力（任务特异性），在不增加计算开销的前提下提升小目标检测能力。此外，采用双向融合策略实现信息双向流动，通过自适应多尺度加权优化特征融合，增强遮挡目标的识别。实验表明，该方法在较大尺寸上相较最优基线提升1.7% mAP@[.5:.95]，同时降低11.9% GFLOPs。所提方法优于现有模型，为实际施工安全监控提供高效可行的解决方案。
原文摘要 · Abstract (English)
Ensuring construction site safety requires accurate and real-time detection of workers' safety helmet use, despite challenges posed by cluttered environments, densely populated work areas, and hard-to-detect small or overlapping objects caused by building obstructions. This paper proposes a novel algorithm for safety helmet wearing detection, incorporating a dynamic attention within the detection head to enhance multi-scale perception. The mechanism combines feature-level attention for scale adaptation, spatial attention for spatial localization, and channel attention for task-specific insights, improving small object detection without additional computational overhead. Furthermore, a two-way fusion strategy enables bidirectional information flow, refining feature fusion through adaptive multi-scale weighting, and enhancing recognition of occluded targets. Experimental results demonstrate a 1.7% improvement in mAP@[.5:.95] compared to the best baseline while reducing GFLOPs by 11.9% on larger sizes. The proposed method surpasses existing models, providing an efficient and practical solution for real-world construction safety monitoring.
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