## 1 Introduction
The automatic inspection system uses sensors, image processing, and data analysis technology to monitor and diagnose faults of electrical equipment in real time [5]. The automatic inspection system can regularly or continuously check the operating status of the equipment, detect and handle faults in a timely manner, and ensure the normal operation of the equipment. Compared with traditional manual inspections, automatic inspections not only improve efficiency but also reduce the occurrence of human errors. For example, in the power system, the automatic inspection system can use drones, robots, and other equipment for automated inspection of substations and power lines to detect and eliminate faults in a timely manner, improving the operational reliability of the power grid [6]. In the automatic inspection process, terminal connection recognition is an important task. Terminal connection recognition involves the accurate detection and classification of cable connectors, which is crucial for ensuring the normal operation and safety of electrical equipment. At present, many industrial sites still rely on manual inspections, which are not only time-consuming and labor-intensive but also prone to misjudgment and missed inspections. Through an automated terminal connection recognition system, inspection efficiency can be significantly improved, human errors can be reduced, and the inspection process can be automated and intelligent [7]. For example, in substation inspection, the terminal connection recognition system can automatically recognize various types of cable connectors, detect their connection status, and ensure the correct connection and safe operation of electrical equipment. Target recognition technology is one of the core technologies to achieve terminal connection recognition. Traditional manual recognition methods are not only inefficient but also susceptible to human factors, leading to misjudgment and missed inspections. By introducing target recognition technologies such as YOLO, DBNet, and CRNN, which have strong feature extraction and pattern recognition capabilities, the automation of terminal connection detection can be realized, reducing human intervention and improving inspection efficiency and accuracy. In the process of machine vision recognition of the connection relationship between terminals and cables, when the camera's shooting angle forms a significant angle with the three-dimensional connection surface of the terminals and cables, it is extremely difficult to judge the connection relationship by relying solely on the direction of the cables in the two-dimensional image, and the accuracy of recognition is low. As shown in Figure 1, in this figure, the straight direction of cable 1 points to terminal 12, and the straight direction of cable 2 points to terminal 17. However, cable 1 and cable 2 are actually connected to terminals 13 and 18, respectively. This picture shows that it is difficult to accurately determine the true connection relationship between the cable and the terminal by simply recognizing the two-dimensional image. Despite the broad application prospects of target recognition technology in industrial electrical automation, there are still some challenges and issues:  
1. Complex backgrounds and occlusions: In practical applications, the terminal connection area may have complex backgrounds and occlusions, which can affect the accuracy of the recognition algorithm. For example, during the inspection of power equipment, cable connectors may be partially obscured by other equipment or obstacles, increasing the difficulty of recognition. 2. Real-time performance and computational efficiency: In the inspection of large-scale electrical equipment, real-time performance and computational efficiency are key considerations. How to improve computational speed and reduce resource consumption while ensuring high accuracy is a hot and difficult point in current research.