Earthquake-Induced Landslide Detection in Remote Sensing Images Using TLSTMF-YOLO

The earthquake-induced landslide targets in remote sensing images vary greatly in size and are unevenly distributed with many small targets. Achieving a balance between high accuracy, computational capability, and small sample size remains challenging. This study proposes to enhance earthquake-induced landslide detection by developing a new algorithm for remote sensing images based on the C3-Swin-Transformer and Multiscale Feature Fusion-YOLO (TLSTMF-YOLO). Utilizing a feature extraction layer and Swin-Transformer structure captures dependencies and preserves spatial information. Introducing the Convolutional Block Attention Module (CBAM) enhances feature representation. Incorporating a Bidirectional Feature Pyramid Network (BiFPN) optimizes bidirectional cross-scale feature fusion, improving landslide detection accuracy across scales. The training utilizes an AdamW optimizer and cosine learning rate strategy for accelerated convergence and improved speed. Transfer learning applies to Jiuzhaigou and Luding landslide datasets. Experimental results show that the TLSTMF-YOLO model outperforms YOLOv5 and other detection models in terms of precision, recall, and mAP@0.5. Specifically, on the Jiuzhaigou dataset, it achieves a precision of 95.7%, a recall of 89.9%, and a mAP@0.5 of 90.5%. On the Luding dataset, it achieves a precision of 96.0%, a recall of 90.9%, and a mAP@0.5 of 94.5%. Additionally, the frame processing times for the TLSTMF-YOLO model are 6.61 ms and 12.2 ms on the Jiuzhaigou and Luding datasets, respectively, demonstrating superior efficiency and confirming its effective feature extraction and fusion capabilities.