InSAR-YOLOv8 for wide-area landslide detection in InSAR measurements

Abstract

Abstract InSAR monitoring technology is widely used in investigating landslide hazards. Leveraging object detection algorithms to quickly extract landslide information from Wide-Area InSAR measurements is of great significance. Our InSAR-YOLOv8, an algorithm that automatically detects landslides from InSAR measurements, addresses the low accuracy and suboptimal detection performance of existing network models. In this method, we first design and add a detection head specifically targeting small-scale objects. This improvement enhances the model’s ability to extract features across different scales and strengthens its capability to detect landslides of varying sizes. We also replace the original C2f module with the lighter C2f_Faster module to process information more efficiently, making the model lighter and more efficient. Finally, the SIoU loss function replaces the CIoU loss function to improve the bounding box regression and enhance detection accuracy. Our results show that the proposed algorithm achieves a 97.41% mAP50, a 66.47% mAP50:95, and a 92.06% F1 score on the InSAR landslide dataset, while reducing the number of parameters by 25%. Compared with YOLOv8 and other advanced models (YOLOvX, Faster R-CNN, etc.), our model exhibits distinct advantages and possesses a wider range of potential applications in InSAR measurement for landslide detection.

Keywords

• Improved YOLOv8
• InSAR measurements
• Automatic detection
• Landslide