Description:
<jats:p>The wide range and high intensity of landslides in the mining area pose a great threat to the safety of human life and property. It is particularly important to identify and monitor them. However, due to the serious surface damage, small landslide scale, complex background and other factors in the mining area, it is impossible to accurately identify and detect the landslide in the mining area. It is necessary to select an efficient detection model to detect it. In this paper, aiming at the problem of landslide identification in mining area, the remote sensing image of mining area is obtained by unmanned aerial vehicle (UAV), and the landslide data set of mining area is constructed by data enhancement method. An improved YOLOv8 algorithm is proposed. By adding a mixed attention mechanism in the channel and spatial dimensions, the detection accuracy of the model for mining landslide is improved, and the monitoring of landslide changes in the mining area is successfully completed. At the same time, an algorithm for locating the landslide position is proposed. Through this algorithm, the detected landslide pixel coordinates can be converted into geodetic coordinates. The results show that the improved YOLOv8 algorithm proposed in this paper has a recognition accuracy of 93.10% for mining area landslides. Compared with the mAP@0.5 of the original YOLOv8 algorithm and YOLOv5 algorithm, the improved YOLOv8 algorithm has an increase of 4.2% and 5.1%. This study has realized the monitoring and positioning of the landslide in the mining area, which can provide the necessary data support for the ecological restoration on mining area.</jats:p>