## 6 Conclusions  
In this paper, geomagnetic detection equipment was adopted to complete the collection of geomagnetic signals of the pressed rigging, and a Kalman filter and YOLOv5 object detection algorithm based on the convolutional neural network model were used to analyze and identify the technical code of the collected pressed rigging defect signals, and the following conclusions were drawn:  
1. In the signal processing stage, the Kalman filter algorithm is used to reduce the noise of the collected geomagnetic signal. In the process of geomagnetic flaw detection, the signal is often affected by many factors such as environmental noise and equipment interference, which leads to more noise interference in the signal. By Kalman filtering, these noises can be removed effectively, which makes the defect signal clearer and enhances the extraction effect of defect features. By comparing the cloud image before and after processing, it can be seen that the noise is significantly reduced and the signal quality is improved, which lays a foundation for the subsequent defect detection. 2. Based on a large number of clamped rigging geomagnetic signals collected, a high-quality defect detection data set is created. In the construction process, the data annotation specification is strictly followed, and each defect location is accurately marked with the annotation method, including the type, location, and size of the defect. This detailed annotation method ensures the high quality of training data, enables the model to fully capture the characteristics of defects in the learning process, and effectively improves the generalization ability of the model. In addition, the construction of data sets provides rich basic data for further study of different types of defect detection. 3. In the stage of target detection, the YOLOv5 algorithm is used to automatically identify the defects of the suppression rigging. To improve the detection effect, the scale detection layer of YOLOv5 has been improved to enhance the detection ability of defects of different sizes. At the same time, the localization loss function is optimized to improve the detection precision and location accuracy of the algorithm. The experimental results show that the improved YOLOv5 model has excellent performance in the detection of defects in the pressed rigging, with the precision reaching 92.5%, the recall reaching 97.2%, the F1 value reaching 97.3%, and the mAP reaching 97.1%. These indicators show that the model can not only efficiently identify the defects in the clamping rigging but can also effectively avoid missing and false detection; it also has high robustness. 4. Compared with traditional defect detection methods, the improved YOLOv5 algorithm shows obvious advantages. Traditional methods rely on manual design features and are limited by manual experience, which makes it difficult to adapt to complex signal environments and diverse defect types. The deep learning model, especially the optimized YOLOv5, can automatically extract and identify defective features through end-to-end learning, which greatly improves the accuracy and efficiency of detection. Compared with the original YOLOv5 model, the optimization scheme proposed in this paper has achieved significant improvement in many indicators such as accuracy, recall rate, and mAP. Methodology, D.Z. and Z.J. ; software, C.H. and Z.L. ; validation, C.H., D.Z. and H.Z. ; investigation, G.Y. and G.Z. ; resources, G.Z. and Z.Y. ; data curation, C.H. and Z.L. ; writing--original draft preparation, C.H. and Z.L. ; writing--review and editing, H.Z. ; supervision, G.Y. and Z.Y. ; project administration, Z.J. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (NSFC, No. 52274338) and a grant from the State Key Laboratory of Metal Material for Marine Equipment and Applications, grant numbers SKLMEA-K202211. Not applicable.