Abstract: Accurate landslide detection is crucial for disaster prevention and early warning.Existing detection methods often suffer from interference by complex backgrounds and small-target features,particularly in high-resolution remote sensing imagery.To address these challenges,this study proposes an enhanced YOLOv8n model incorporating the Convolutional Block Attention Module (CBAM) for landslide detection in high-resolution remote sensing images (YOLOv8n-CBAM),validated using high-resolution remote sensing datasets.The model demonstrates three key advantages:(1)It significantly enhances attention to critical features in complex terrains and vegetation-covered scenarios,improving detection accuracy for small landslides while reducing missed and false detections.(2)Evaluation metrics including normalized confusion matrix,precision-recall curves,and loss curves confirm superior detection capability,accuracy,and robustness compared to the baseline YOLOv8n model.(3)Comparative analysis reveals optimal performance in precision (89.7%),recall (86.4%),mAP@0.5 (90.1%),and mAP@0.5:0.95 (67.3%) metrics among state-of-the-art detection methods.