Landslides are geological disasters that severely threaten ecological environments and human safety. Efficient and accurate identification for landslides is crucial for disaster monitoring and early warning systems. To address the issues of low accuracy and high false detection rates in traditional object detection algorithms for landslide identification, a landslide detection method was proposed based on an improved YOLOv7 model. The specific approach includes fouows as these The YOLOv7 network was optimized to improve its feature extraction for landslide imagery; the convolutional block attention module (CBAM) was integrated, which improves the accuracy of detecting landslide regions; a new loss function, MPDIoU, was introduced. This function improves overall detection accuracy by reducing both false alarms and missed detections. Experimental results demonstrate that, compared with the original YOLOv7, the proposed method improves the mean average precision (mAP) by 6.3%, enhances precision by 5.8%, and reduces the model’s parameter size by 6.11 M. The method is capable of efficiently detecting landslide bodies in high-resolution remote sensing images and has strong practical application value. It can provide effective technical support for geological disaster monitoring and early warning systems.