# CGALS-YOLO: Vision-Based Sensing for Protective Equipment Wearing Compliance Detection in Underground Environments

## TL;DR

This paper introduces CGALS-YOLO, a vision-based system to detect if workers in underground mines are wearing protective equipment correctly, improving safety monitoring.

## Contribution

The novel CGALS-YOLO model integrates content-guided feature fusion and a lightweight detection structure for better accuracy in complex underground mining environments.

## Key findings

- CGALS-YOLO improves detection accuracy by 4.6% and recall by 3.1% compared to YOLOv8n.
- The model achieves an mAP@0.5 of 89.4% on an underground protective equipment dataset.
- The proposed method reduces parameters by 23.9% while maintaining detection performance.

## Abstract

Reliable vision-based sensing of protective equipment wearing compliance is essential for safety monitoring in underground mining environments, where complex lighting conditions, similar background textures, and large variations in the scale of wearable items significantly degrade detection performance. To address these challenges, this study proposes a vision-based protective equipment wearing compliance detection method for underground personnel based on CGALS-YOLO. Traditional object detection models often introduce substantial redundant background information during multi-scale feature fusion, which weakens the perception of key wearing regions, particularly for small-scale targets. To alleviate this issue, a content-guided feature fusion (CGAFusion) module is incorporated into the neck of the YOLOv8 network, enabling adaptive fusion of same-scale multi-path features through the collaborative effects of channel, spatial, and pixel attention mechanisms. This design enhances target-related feature representation while suppressing background interference in complex underground scenes. Furthermore, to reduce parameter redundancy and improve cross-scale discrimination consistency in the detection head, a lightweight shared convolution detection (LSCD) structure is introduced. By employing cross-scale shared convolution parameters, group normalization, and scale-adaptive regression, the proposed model achieves a parameter reduction of approximately 23.9% while lowering computational complexity and maintaining stable multi-scale detection performance. Experimental results on an underground protective equipment wearing compliance dataset demonstrate that CGALS-YOLO improves detection accuracy by approximately 4.6% and recall by 3.1% compared with the baseline YOLOv8n, achieving an mAP@0.5 of 89.4%. These results validate the effectiveness and practical applicability of the proposed method for real-time vision-based safety monitoring in underground environments.