# HS-YOLO: Small Object Detection for Power Operation Scenarios
## 1 Introduction  
Power operation sites often have potential hazards such as high-voltage power lines, equipment, and work at heights, which may cause serious injury or even endanger life in case of accidents. Violations by workers, such as failure to wear safety gear, crossing safety barriers, or making operational errors, are common causes of safety incidents [1]. Therefore, effective monitoring of workers' violations is crucial to ensure the safety and stability of power operations. In this context, technologies in related fields such as image processing are progressively integrating into the domain of electric power operations [2]. Through methods such as object detection, object recognition, and behavioral analysis, these technologies enable real-time capture of personnel activities and analysis of real-time images. It helps to automatically identify and monitor the violations of operators. The power industry is placing increasing demands on monitoring efficiency, accuracy, and intelligent capabilities, with a large number of intelligent monitoring technologies being applied to power operation scenarios [3]. The images collected from the power operation scenarios are automatically analyzed and processed by intelligent monitoring. Specific objects such as safety belts, safety helmets, and seines in the monitoring images are precisely located and classified after the critical information and features are extracted. This facilitates a more accurate analysis of personnel behavior involved in the operations [4, 5]. It is evident that object detection is highly crucial in intelligent monitoring, but the existing methods may not be entirely suitable for power operation scenarios. The continuously changing distances and angles between surveillance cameras and detection objects result in varying sizes of detected objects in images. This requires the algorithm to be able to adaptively extract features suitable for different object sizes and maintain the capability to detect and locate objects across varying scales. This adaptability ensures the accuracy and stability of detection under diverse circumstances. The growing attention on safety issues in the power industry has led many researchers to put forward deep-learning detection methods suitable for this industry. These methods have demonstrated varying degrees of success, but they also show certain limitations. For example, they may lack the necessary sensitivity to cope with the complex environment in small object detection scenarios, and in the face of small object scales may exhibit sub-optimal performance when changed. Addressing these shortcomings is critical to improving the effectiveness of such methods. [PERSON] et al. [6] incorporated object relationship modules into the detection module, paying attention to the interconnection between different objects, and optimized the loss function design, resulting in more accurate segmentation of slender and flexible objects such as seines and safety belts in the context of power operation scenes. [PERSON] et al. [7] proposed the R_YOLOV5 detection algorithm, which introduced rotated bounding boxes (RBB) into the detection network to address the issue of background aspect ratios and made targeted improvements to flexible objects in any direction in power operation scenes. However, these studies primarily aimed at improving the detection of certain types of regular-sized objects while overlooking the existence of small objects in power operation scenes. [PERSON] et al. [8] focused on the detection of insulators and defects in power systems. They compressed the model size using the Ghost module and added an attention module to focus on key information about small objects. [PERSON] et al. [9] introduced an attention mechanism to adjust the network to pay more attention to the surrounding areas of the object, optimized the loss function according to the characteristics of the object, and proposed a lightweight network for detecting small transmission lines in aerial images.