## 2 Unsafe Behavior Identification Model  
### Overview of the YOLOv5s Model  
As an optimized iteration of YOLOv4, YOLOv5 demonstrates substantial advancements in both computational efficiency and detection precision [21]. The architecture offers four primary configurations (YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x), catering to diverse computational constraints and speed-accuracy tradeoff requirements. Among these variants, YOLOv5s represents the most lightweight implementation specifically engineered for applications demanding real-time processing capabilities and minimal computational overhead. Notably, this compact model maintains an optimal equilibrium between detection accuracy (54.7% [EMAIL_ADDRESS]) and inference speed (142 FPS on Tesla V100), rendering it particularly suitable for time-sensitive safety-monitoring tasks [22].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Backbone** & \multirow{2}{*}{**Detection Limitations**} & \multirow{2}{*}{**Computational Efficiency**} & \multirow{2}{*}{**Deployment Challenges**} \\  & & & & & 45 FPS on RTX 2080 Ti & - & No native TensorRT support \\ YOLOv3 & & & \textless{} 80 \textless{} 80 pixels & - & 236 MB model size & - & Limited quantization \\  & & - & Poor scale invariance in & & & capabilities \\  & & - & cluttered scenes & & & \\ \hline \multirow{4}{*}{YOLOv4} & \multirow{4}{*}{CSPDarknet53} & \multirow{4}{*}{-} & 19\% false positives under lighting variations & - & 38 FPS with 42.7 GFLOPs & - & High VRAM requirements (\(\geq\)8 GB) \\  & & - & 34\% recall drop at & & - & Complex multi-GPU synchronization \\  & & 50m+ distances & & & & synchronization \\ \hline \multirow{4}{*}{YOLOv5} & \multirow{4}{*}{
\begin{tabular}{c} CSPNet- \\ based \\ \end{tabular} } & - & 12\% mAP degradation for & - & 83 FPS with 16.4 GFLOPs & - & PyTorch-exclusive deployment \\  & & occluded PPE & - & 14.4 MB (s-model) & - & ARM optimization requiring \\  & & (\textgreater{}30° yaw) & & & & ONNX conversion \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative limitations of YOLO architectures in construction safety contexts.  
Input layer: processes variable-resolution input images through adaptive scaling, standardizing dimensions to 640 \(\times\) 640 pixels via letterbox resizing  
Backbone layer: uses a CSPDarknet53 variant as the core feature extractor, using cross-stage partial connections to enhance gradient flow, while reducing parameter count  
Neck layer: incorporates a path aggregation network (PANet) structure with bidirectional feature pyramid connections, enabling multi-scale feature fusion from different network depths  
Head layer: generates final predictions through three detection branches (20 \(\times\) 20, 40 \(\times\) 40, 80 \(\times\) 80 grids), outputting bounding box coordinates (x, y, w, h), object confidence scores, and class probabilities  
The YOLOv5s model demonstrates notable efficiency in object detection tasks, maintaining an effective speed-accuracy balance on benchmark datasets, like COCO. Nevertheless, its deployment for monitoring construction workers' unsafe behaviors in operational construction environments reveals three principal limitations: (1) suboptimal convergence properties of the default loss function during bounding box regression, (2) insufficient discriminative power in feature representation for complex construction scenarios, and (3) subpar performance in small-object detection (typically below 40% recall rate for sub-50 px targets)--a critical requirement for identifying distant workers or safety equipment details in surveillance footage [23].