Improved YOLOv5s for large-scale InSAR mining deformation detection
Regarding the challenges of low efficiency and the inability to promptly identify geological disasters in extensive mining areas through traditional InSAR full-process processing,this paper focuses on InSAR interference fringes and proposes a method for detecting deformation interference fringes in mining areas across a large-scale observation range,based on the improved YOLOv5s.The network model is trained using transfer learning methods,incorporating the ECA attention mechanism and ASPP module,and a small target network prediction layer is added to enhance the accuracy of detecting deformation interference fringes.The improved YOLOv5s achieves an average accuracy of 94.6％,surpassing YOLOv5s,SSD,Faster RCNN,YOLOv3,and YOLOv7 by 2.4％,5.7％,4.4％,2.7％,and 3.2％,respectively.The F1 Score reaches 89.9％,indicating increases of 3.9％,6.9％,17.7％,2.4％,and 2.6％,respectively.The accuracy of detecting deformation interference fringes in the research area is 92.7％,with a false detection rate of 4.3％.Experimental results demonstrate that this method can not only accurately identify deformations in extensive mining areas,promptly identify disaster hazards,and evaluate and issue warnings,but also significantly enhance the efficiency of traditional InSAR deformation monitoring.
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