High-precision landslide detection algorithm based on improved YOLOv11n

Landslide monitoring is a crucial component of geological disaster early warning systems. Traditional landslide detection methods often suffer from insufficient accuracy or low efficiency. To address these issues, this study proposes an improved landslide detection algorithm based on YOLOv11n, aiming to enhance both detection accuracy and efficiency by optimizing the model structure. First, the GhostConv module is introduced to reduce redundant computations, thereby improving computational efficiency. Additionally, the C3K2-SCConv optimization module is incorporated, which enhances feature extraction capability and improves the recognition of landslides at different scales by integrating multi-scale information and a weighted convolution strategy. Furthermore, the SimAM attention mechanism is implemented to adaptively adjust feature map weights, strengthening key features in landslide regions and improving detection accuracy. Experimental results demonstrate that the improved model achieves a mean average precision (mAP@0.5) of 83.3%, a precision of 85.5%, and a recall of 78.1%, representing increases of 2.0%, 3.2%, and 2.8%, respectively, compared to the baseline model. The proposed improvements provide a more accurate and efficient landslide detection method, contributing to the precision of geological disaster early warnings and enhancing the reliability of disaster prevention and mitigation efforts.