Dynamic Attention and Bi-directional Fusion for Safety Helmet Wearing Detection

TL;DR

This paper introduces a novel detection algorithm for construction safety helmets that uses dynamic attention and bi-directional feature fusion, significantly improving accuracy and efficiency in complex environments.

Contribution

It presents a new algorithm combining dynamic attention mechanisms and bidirectional feature fusion, enhancing small object detection and occlusion handling without extra computational cost.

Findings

1.7% improvement in mAP@[.5:.95] over baseline

11.9% reduction in GFLOPs on larger models

Superior performance in real-world construction safety monitoring

Abstract

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.