# A Real-Time DAO-YOLO Model for Electric Power Operation Violation Recognition
In recent years, computer vision and artificial intelligence technologies have emerged as predominant solutions in EPOVR applications [1]. The proposed approaches not only markedly enhance monitoring efficiency but also substantially lower labor expenses, while delivering robust technical support for intelligent safety oversight in power operations. They play a vital role in safeguarding power operators and improving industry regulatory standards. Object detection techniques have seen remarkable advancements in recent years, driven by progress in deep learning [2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12]. The intelligent detection of EPOVR mainly relies on object detection technology [13]. The detection of critical safety equipment such as safety harnesses, helmets, and reflective rests in power utility operations enables effective identification of safety violations, thereby facilitating real-time monitoring and proactive warning systems. The You Only Look Once (YOLO) series [14; 15; 16; 17] of object detection algorithms have demonstrated notable success in various industrial applications, showing particular promise in EPOVR scenarios. Since its initial release, the YOLO object detection model has undergone continuous development through numerous iterations, spanning from YOLOv1 to YOLOv11. This study demonstrates through a comprehensive comparative analysis (as detailed in Section 4.5) illustrating that the YOLOv8 model most effectively addresses the operational requirements of power companies. This capability stems from the demonstrated effectiveness of YOLO series models in fulfilling real-time processing demands across physical deployment environments [18]. Moreover, YOLOv8 not only offers efficient real-time detection capabilities but also delivers more accurate detection results, which is crucial for enhancing the accuracy of safety supervision in power operations. Although the YOLOv8 model has achieved favorable detection performance in multiple application scenarios, it still encounters the following challenges in EPOVR:  
1. The feature representation capability for objects with irregular shapes is insufficient. As shown in Figure 1a,b, the YOLOv8 model fails to detect hooks and cranes with irregular shapes. 2. The feature representation capability for multi-scale objects is insufficient. As shown in Figure 1a,b, the YOLOv8 model fails to detect smaller hooks and larger cranes. 3. The localization accuracy is insufficient. As shown in Figure 1b, the bounding box produced by the YOLOv8 model is too small for the operator. Figure 1: Detection results of YOLOv8 and the proposed DAO-YOLO models. **(a,b)** are from the EPOVR-v1.0 dataset. 4. The current datasets are not sufficient for EPOVR. The current datasets usually focus on safety helmets and reflective vests, as shown in Figure 2. These tasks are relatively less challenging, and existing studies are relatively mature in these tasks. However, many violations cannot be covered by the above object categories, such as personnel standing under lifted loads, workers throwing objects from height, and ladders without height restriction marking, etc. In order to address the above challenges, a novel object detection model, named DAO-YOLO, is proposed for EPOVR. The unique contributions of DAO-YOLO are as follows:  
1. To address the first challenge, a deformable C2f (DC2f) module is proposed and integrated into the backbone network. The DC2f module replaces the traditional convolutional operations with deformable convolution (DConv) and depthwise separable convolution operations. This approach reduces the number of parameters while providing more accurate feature representation for objects with irregular shapes. 2. To address the second challenge, an adaptive multi-scale feature enhancement (AMFE) module is proposed and integrated into the backbone network. The AMFE module combines multi-scale depthwise separable convolutions with an adaptive channel attention mechanism to better represent the characteristics of multi-scale objects. 3.