## 3 Materials and Methods
### The Edge-YOLO Architecture  
The objective of this study is to detect compliance violations in the safety gear of power construction workers, including safety helmets, safety belts, work clothes, and safety gloves. Given that the system is intended to be deployed on edge devices, the real-time performance of the detection model is critical. Additionally, due to the unique nature of power construction safety detection, the system must operate continuously once \begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Parameters** \\ \hline GPU & 384-core NVIDIA Volta \({}^{\text{TM}}\) with 48 Tensor cores \\ CPU & six-core NVIDIA Carmel ARMv8.2 64-bit CPU \\ Memory & 16 GB 128-bit LPDDR4x@1600 MHz \\ Data storage & 16 GB eMMC5.1 \\ CSI & CSI support for up to 6 cameras (14 channels) MIPI CSI-2 D-PHY \\  & 1.2 \\ PCIE & 1 \(\times\) 1 (PCIE3.0) + 1 \(\times\) 4 (PCIE4.0), 144 GT/s \\ Video encoding/decoding & 2 K \(\times\) 4 K 60 Hz encoding (HEVC); 2K4K60 Hz decoding \\ Size & 69.6 mm \(\times\) 45 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameters of Jetson Xavier NX. activated. Thus, the accuracy and stability of the model become indispensable. In this paper, we present an innovative deep learning network architecture, Edge-YOLO, which is an improved version of YOLOv5s. Our proposed model is specifically designed for real-time monitoring of power construction sites and is capable of detecting non-compliant worker behavior related to safety regulations. The YOLOv5 algorithm is a popular object detection model optimized for GPU computing. It is the fifth iteration of the original YOLO algorithm and includes several variations based on the width and depth of the network. The latest version, YOLOv5 7.0 [32], includes YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. Among these variations, the YOLOv5s model has been specifically designed to streamline the network and achieve high speed on edge devices, such as the Jetson Xavier NX, with a frame rate of up to 30 FPS. Additionally, the YOLOv5s model offers several advantages, including high detection accuracy, stability, and ease of deployment. Notably, it outperforms YOLOv5n, YOLOv7-tiny, and YOLOv8n in terms of both accuracy and stability. Therefore, a model based on YOLOv5s is well-suited for deployment on the Jetson Xavier NX and is an ideal choice for an intelligent power construction compliance wearing detection system. The YOLOv5s model consists of three primary components: backbone, neck, and prediction (as illustrated in Figure 1). The entire working process of the YOLOv5s network can be described in detail as follows. 1. Backbone: The backbone network comprises a Con structure, Batch Normalization (BN), and SiLu operations. It includes CSP1_X blocks and a SPPF (Spatial Pyramid Pooling-Fast) module. CSP1_X blocks concatenate different convolutions to extract valuable features from the input image. The network applies a Convolution module with a size of 3 and a step size of 2 to extract image features, producing an output image of size 160 \(\times\) 160 \(\times\) 128. Three groups of CSP1 and Conv layers further enhance feature extraction, resulting in a feature map of size 20 \(\times\) 20 \(\times\) 1024. A bottleneck structure improves detection accuracy. An SPPF module is incorporated to enhance the accuracy of the 20 \(\times\) 20 feature map, capturing features at multiple scales and improving object detection accuracy. 2. Neck: The Neck module, comprising CSP1_X and CSP2_X, plays a critical role in balancing network speed and accuracy. CSP2_X reduces model parameters while improving precision. Additionally, the Neck also includes an upsampling process that upsampled feature maps of size 80 \(\times\) 80 \(\times\) 512 using two sets of CSP2_X, Conv, Upsample, and Concat connections of size 1 and step 1. As a result, the network produces feature maps of 80 \(\times\)80 \(\times\)512, 40 \(\times\) 40 \(\times\) 512, and 20 \(\times\) 20 \(\times\) 512 after subsampling the initial 80 \(\times\) 80 \(\times\) 512 feature map. 3.