An Improved YOLOv5s-Based Algorithm for Unsafe Behavior Detection of Construction Workers in Construction Scenarios

Currently, the identification of unsafe behaviors among construction workers predominantly relies on manual methods, which are time-consuming, labor intensive, and inefficient. To enhance identification accuracy and ensure real-time performance, this paper proposes an enhanced YOLOv5s framework with three strategic improvements: (1) adoption of the Focal-EIoU loss function to resolve sample imbalance and localization inaccuracies in complex scenarios; (2) integration of the Coordinate Attention (CA) mechanism, which enhances spatial perception through channel-direction feature encoding, outperforming conventional SE blocks in positional sensitivity; and (3) development of a dedicated small-target detection layer to capture critical fine-grained features. Based on the improved model, a method for identifying unsafe behaviors of construction workers is proposed. Validated through a sluice renovation project in Jiangsu Province, the optimized model demonstrates a 3.6% higher recall (reducing missed detections) and a 2.2% mAP improvement over baseline, while maintaining a 42 FPS processing speed. The model effectively identifies unsafe behaviors at water conservancy construction sites, accurately detecting relevant unsafe actions, while meeting real-time performance requirements.