In response to issues with existing classical semantic segmentation models, such as inaccurate landslide edge extraction in high-resolution images, large numbers of network parameters, and long training times, this paper proposes a lightweight landslide detection model, Landslide Detection Network (LDNet), based on DeepLabv3+ and a dual attention mechanism. LDNet uses the lightweight network MobileNetv2 to replace the Xception backbone of DeepLabv3+, thereby reducing model parameters and improving training speed. Additionally, the model incorporates a dual attention mechanism from the lightweight Convolutional Block Attention Module to more accurately and efficiently detect landslide features. The model underwent dataset creation, training, detection, and accuracy evaluation. Results show that the improved LDNet model significantly enhances reliability in landslide detection, achieving values of 93.37%, 91.93%, 92.64%, 86.30%, 89.79%, and 95.28% for precision (P), recall (R), F1-score (F1), intersection over union (IoU), mean IoU (mIoU), and overall accuracy (OA), respectively, representing improvements of 14.81%, 13.25%, 14.02%, 14.58%, 14.27%, and 13.71% compared to the original DeepLabv3+ network. Moreover, LDNet outperforms classical semantic segmentation models such as UNet, PSPNet, DeepLabv3+, HRNet and Swin Transformer in terms of recognition accuracy, while having significantly fewer parameters and shorter training times. The model also demonstrates good generalization capability in tests conducted in other regions, ensuring extraction accuracy while significantly reducing the number of parameters. It meets real-time requirements, enabling rapid and accurate landslide detection, and shows promising potential for widespread application.