[PERSON] designed testing equipment that uses four residual magnetic field sensors uniformly arranged around the steel wire rope. This device can detect the steel wire rope without magnetization or special preparation, which greatly reduces the weight of the detection probe [10]. The TGS flaw detector jointly developed by the Harbin Institute of Technology and Fushun Branch of China Research Institute of Coal Science can simultaneously detect LF (broken wire) and LMA (metal cross-section loss) damage defects in steel wire rope [11]. [PERSON] et al. developed a new type of wire rope flaw detection sensor based on the open magnetization method, adopted the damage detection method of fluxgate technology, and captured three-dimensional magnetic leakage information from the surface of the wire rope with the help of the Hall sensor array [12]. However, there are still some limitations in the detection accuracy, efficiency, and adaptability of the above equipment through magnetic detection. In recent years, with the rapid development of deep learning technology, convolutional neural networks (CNNs) have shown strong capabilities in image recognition, object detection, and other fields, especially in automatic detection. [PERSON] et al. used a multi-channel magnetic flux leakage sensor probe to scan the wire rope and proposed a method that combined the scale damage index with the multilevel pattern recognition network to realize the automatic evaluation of the damage degree of the wire rope [13]. [PERSON] et al. proposed an ELM algorithm based on the single hidden-layer feedforward neural network (SLFNN) and verified that the algorithm has faster classification speed and higher classification accuracy for wire rope with different breaking degrees [14]. [PERSON] et al. used transfer learning to improve the training accuracy of the wire rope sample model, effectively improving the accuracy of target detection [15]. You Only Look Once (YOLO), as a typical object detection algorithm, realizes efficient object detection by dividing the image into multiple grids and predicting the position and category of the object on each grid. Compared with traditional target detection methods, YOLO has obvious advantages in detection speed and accuracy, especially for real-time detection scenarios. In the detection of defects in pressed rigging, the YOLO algorithm can be used to identify and locate defects in pressed rigging, such as broken wire, wear, and rust [16]. Specifically, by convolution processing of the surface image of the pressed rigging, the features in the image are extracted first, and these features are analyzed by the neural network, and the position and category of the defects are finally output. Compared with traditional detection methods, the adoption of the YOLO algorithm not only improves the accuracy of detection but also significantly improves the speed of detection, enabling real-time detection of pressed rigging under service conditions [17]. At present, experts and scholars around the world have conducted a lot of research and exploration of the relevant theories, methods, and equipment of suppression rigging detection and have made a lot of contributions to the safety detection of suppression rigging [18; 19; 20; 21]. However, due to the different structures, damage types, damage locations, and different working conditions, the accuracy of the defect detection of the suppression rigging is seriously interfered with. At present, there is still a lack of systematic evaluation and methods suitable for the safety of the pressed rigging, and the existing instruments and equipment are only used as manual auxiliary inspection measures to assist in the detection of the pressed rigging. Therefore, accidents caused by the cracking of the suppression rigging also occur from time to time and have caused serious consequences [22].