# A Real-Time DAO-YOLO Model for Electric Power Operation Violation Recognition
## 2 Literature Review  
In recent years, deep learning technology-based automated object detection methods have been widely applied in diverse scenarios, driving remarkable efficiency improvements and delivering substantial practical value. For instance, deep learning is used to classify building facade materials from street-view images [21], enhancing automation capabilities in urban analysis. Similarly, deep learning-based object detection has played a crucial role in EPOVR and has become the predominant approach in this field. Current object detection methods for EPOVR can be broadly categorized into three types: YOLOv5 model-based  
Figure 2: Illustrations of the detection of safety helmets and reflective vests. (**a**,**b**) are cited from the public datasets SHWD [19] and PPE [20], respectively. methods, YOLOv8 model-based methods, and the other YOLO model-based methods, with the majority of methods belonging to the first two types. Below is a brief introduction to each type. Firstly, many YOLOv5-based methods for EPOVR have been introduced. [PERSON] et al. [22] proposed an improved YOLOv5 model for detecting power operation violations. The method introduces a spatial attention mechanism to enhance feature extraction in cluttered scenes, and employs Bayesian-optimized multi-scale fusion to boost detection accuracy across varying object sizes. [PERSON] et al. [23] proposed an improved YOLOv5-based algorithm for detecting safety equipment in power operations, introducing a polarized self-attention module to enhance feature extraction and replacing traditional convolution with grouped spatial convolution to reduce model complexity, thereby improving computational speed and detection accuracy. [PERSON] et al. [24] proposed a lightweight YOLOv5-based safety helmet detection algorithm, embedding a multispectral channel attention mechanism for small-target enhancement, and using channel pruning to achieve real-time detection on embedded devices. Other related works include YOLO-M3C [25], HR-YOLO [26], and FEFD-YOLOV5 [27]. YOLOv8-based methods for EPOVR have also been extensively studied. [PERSON] et al. [28] introduced a helmet detection approach utilizing PConv-YOLOv8, where partial convolution is embedded in the YOLOv8 backbone, a similarity-based attention mechanism is incorporated into the neck, and the Wise-Distribution Focal Loss is employed to enhance detection performance. [PERSON] et al. [29] developed an enhanced helmet detection method based on YOLOv8, where the original C2f module is substituted with the C2f-FE module, incorporating partial convolution from FasterNet and an efficient multi-scale attention mechanism to improve detection accuracy. [PERSON] et al. [30] presented a personal protective equipment detection method by modifying YOLOv8s through the integration of a reparameterization module in its backbone network and the incorporation of an R-C2F module alongside a multi-scale feature fusion architecture. [PERSON] et al. [31] proposed an improved YOLOv8-based model for detecting non-personal protective equipment on construction sites, utilizing multiple data augmentation techniques to expand the size of training samples, and analyzing the impact of data augmentation on model performance across different classes and backbone networks. Other related works include SDCB-YOLO [32], MEAGYOLO [33], YOLOv8s-SNC [34], and YOLOv8-ADSC [35]. Finally, other methods based on different YOLO models for EPOVR have also been investigated. [PERSON] et al. [36] developed an enhanced detection model based on YOLOv9 for safety protective equipment in power operations, incorporating self-calibrating convolutions to expand receptive fields and a wavelet transform downsampling module to preserve feature integrity. [PERSON] et al.