## 1 Introduction  
As the core component of power grid equipment, transmission and distribution lines are essential in connecting all kinds of equipment. To ensure the safe and reliable operation of transmission and distribution lines, equipment maintenance should be carried out regularly to eliminate potential equipment defects and safety hazards [1]. Manual inspection has been mainly used in power grid equipment operation and maintenance. However, due to the wide distribution of transmission and distribution line equipment and the harsh operating environment, various failures occur occasionally. In recent years, the use of inspection robots, drones, Unmanned Aerial Vehicles (UAVs), and helicopters has been increasing [2]. However, the cost of helicopter inspection is too high, and safety cannot be guaranteed. UAVs can effectively reduce the workforce and eliminate the interference of human factors, but the detection of broken parts in transmission lines is vulnerable to complex backgrounds and obstacles. With its safety characteristics, high efficiency, accuracy, and reliability, a line patrol robot provides an effective solution for maintaining transmission and distribution line equipment [3]. The inspection robot is equipped with sensors and cameras to locate the transmission line; identify the shock-proof hammer, spacer rod, suspended wire clamp, and other tools on the line; and then execute the corresponding crossing procedure to carry out online inspection, discover obstacles, and collect data [4]. With the continuous deepening and optimization of Convolutional Neural Networks (CNNs) on the convolutional layer, deep learning algorithms based on classification and object detection have also been widely used in hardware detection [5]. Deep learning algorithms mainly extract and learn the features of the obstacle target through evolving deep neural networks, meaning the extracted feature information is independently analyzed, which is not restricted by humans but rather explores various details of interest in the object obstacle [6]. Object detection algorithms based on deep learning can be roughly divided into one-stage and two-stage algorithms. In the two-stage detection process, features are extracted from the image to generate some candidate regions, and then the candidate regions are classified and positioned to output the location and category information of the target [7]. Examples of typical two-stage detection algorithms include Region-CNN (R-CNN) [8], Fast R-CNN [9], and Faster R-CNN [10]. In one-stage detection, CNN is used to extract image features. The extracted features are transmitted to multiple fully connected layers for target detection, and then the position and category information of the target are directly output. [PERSON] et al. proposed a live power transmission detection method based on R-CNN, which uses the CNN to extract visual features from the aerial images. This method can accurately detect insulators in different environments and accurately detect the faulty insulators in the image [11]. [PERSON] et al. applied the Fast R-CNN to an automatic fish identification system to help marine biologists estimate fish existence and quantity, and effectively understand oceanic geographical and biological environments [12]. Moreover, deep learning was investigated to intelligently detect road cracks, and Faster R-CNN and Mask R-CNN were compared and analyzed. The results show that the joint training strategy was very effective, and that both Faster R-CNN and Mask R-CNN completed the crack detection task when trained with only about 130 images [13]. Common one-stage algorithms are the Single Shot Multibox Detector (SSD) and the You Only Look Once (YOLO) series [14; 15; 16; 17; 18; 19; 20; 21]. Various one-stage algorithm models have been utilized for fault detection in transmission and distribution lines. [PERSON] et al. studied fault location, detection, and classification in transmission lines based on the SSD model [22]. [PERSON] et al.