A Lightweight Anchor-Free Subsidence Basin Detection Model With Adaptive Sample Assignment in Interferometric Synthetic Aperture Radar Interferogram

Abstract

The excessive exploitation of coal resources has caused serious land subsidence, which seriously threatens the lives of the residents and the ecological environment in coal mining areas. Therefore, it is of great significance to precisely monitor and analyze the land subsidence in the mining area. To automatically detect the subsidence basins in the mining area from the interferometric synthetic aperture radar (InSAR) interferograms with wide swath, a lightweight model for detecting the subsidence basins with an anchor-free and adaptive sample assignment based on the YOLO V5 network, named Light YOLO-Basin model, is proposed in this paper. First, the depth and width scaling of the convolution layers and the depthwise separable convolution are used to make the model lightweight to reduce the memory consumption of the CSPDarknet53 backbone network. Furthermore, the anchor-free detection box encoding method is used to deal with the inapplicability of the anchor box parameters, and an optimal transport assignment (OTA) adaptive sample assignment method is introduced to solve the difficulty of optimizing the model caused by abandoning the anchor box. To verify the accuracy and reliability of the proposed model, we acquired 62 Sentinel-1A images over Jining and Huaibei coalfield (China) for the training model and experimental verification. In contrast with the original YOLO V5 model, the mean average precision (mAP) value of the Light YOLO-Basin model increases from 45.92 to 55.12%. The lightweight modules of the model sped up the calculation with the one billion floating-point operations (GFLOPs) from 32.81 to 10.07 and reduced the parameters from 207.10 to 40.39 MB. The Light YOLO-Basin model proposed in this paper can effectively recognize and detect the subsidence basins in the mining areas from the InSAR interferograms.