Detection and Classification of Multi-Magnetic Targets Using Mask-RCNN

To detect the shape of a small magnetic target in the shallow underground layer, this article proposes a recognition method based on Mask-RCNN. Firstly, using COMSOL software and MATLAB software to establish the database of magnetic targets model under different shapes and orientations, which greatly enriched the diversity of the training data set. Then, the ${G}_{\mathrm {zz}}$ component of the magnetic gradient tensor matrix is selected to highlight the shape features of the magnetic target, and the contour image is generated. The experimental data set is created by using the deep learning annotation tool Labelme. Finally, Resnet101 is used as the backbone network and feature pyramid network (FPN) structure is used to extract features. The regional recommendation network (RPN) is trained end-to-end to create regional recommendations for each feature map. The detection results of 200 test images show that the average detection accuracy of the method is 97%, and the recall rate is 94%. The simulation results show that the recognition accuracy and robustness of the method are improved.