###### Abstract  
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-EloU 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 shuice 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. s _article_supdated  
Several innovative approaches have emerged in this domain: [PERSON] et al. [6] developed a vision-based framework focusing specifically on unsafe ladder-climbing behaviors through automated action monitoring, though its application scope remains limited. [PERSON] et al. [7] implemented an enhanced YOLOv3-DN algorithm for hazard identification with BIM integration, yet their methodology neglects behavioral analysis of workers. In contrast, [PERSON] et al. [8] created an intelligent recognition system for underground coal mines, combining OpenPose, YOLOv3, and MobileNetV3 models, achieving successful detection of multiple unsafe behaviors despite suffering from high computational complexity and suboptimal processing speed. Specific safety equipment violations have attracted particular attention: [PERSON] et al. [9] proposed a deep learning model to detect missing safety harnesses during elevated work, while [PERSON] et al. [10] used the Faster R-CNN model for helmet-use compliance monitoring across diverse conditions. For complex environmental challenges, [PERSON] et al. [11] introduced a hybrid CNN-LSTM model capable of extracting and analyzing intricate image features for behavior recognition. Beyond visual monitoring, [PERSON] et al. [12] integrated EEG signals with deep learning to develop a wireless fatigue detection system, providing proactive prevention of safety incidents. Demonstrating cross-industry applicability, [PERSON] et al. [13] adapted the YOLOv3 algorithm for gas station safety, achieving 67-85% accuracy in detecting critical violations, like smoking and phone usage. While earlier YOLO architectures, like YOLOv3 and YOLOv4, demonstrate initial success in general object detection, three critical limitations emerge when applied to construction safety monitoring: First, the Darknet-53 backbone in YOLOv3 exhibits insufficient feature extraction capability for small-scale safety gear (e.g., unstrapped helmets < 50 \(\times\) 50 pixels). Second, YOLOv4's CSPDarknet53 structure, despite its enhanced accuracy, increases computational complexity (42.7 GFLOPs vs. YOLOv5s' 16.4 GFLOPs), hindering real-time deployment on edge devices. Third, both models lack dedicated mechanisms to address the signature challenges of construction sites--severe occlusions in complex environments and lighting variations across shift changes. These operational constraints motivated our adoption of YOLOv5s as the baseline, which provides an optimized architecture with (a) adaptive anchor box scaling through k-means++ clustering, (b) streamlined neck design via PANet+FPN fusion, and (c) native support for hardware-aware quantization--critical prerequisites for field deployment.