Intelligent Detection Method for the Wearing Status of Safety Helmet Chin Straps at Construction Sites

The proper wearing of safety helmets is critical for worker safety in high-risk construction environments, with the fastening of the chin strap serving as a key indicator of correct usage. However, existing detection methods primarily focus on identifying helmet presence, neglecting the crucial assessment of chin strap compliance. This paper proposes an intelligent detection approach that integrates YOLOv8 object detection, instance segmentation, and skin tone recognition to evaluate chin strap wearing status. The system first employs YOLOv8 to detect workers and helmets, filtering out non-wearers before performing facial and neck region segmentation, thereby concentrating computational resources on compliance verification. To address challenges in distinguishing chin straps from similar skin tones under complex lighting conditions, the method incorporates illumination compensation and YCbCr-based skin segmentation. Finally, strap status is determined through morphological operations and contour analysis, with visual annotation of the detection results. This study utilizes a dataset comprising 2000 safety helmet images, which was partitioned into training, validation, and test sets in an 8:1:1 ratio for model training and evaluation. The experimental results demonstrate that the proposed method achieves an accuracy of 96% in detecting chin strap status, exhibits robust performance across diverse construction site conditions, and holds significant practical value and application potential.