A novel Dynahead-Yolo neural network for the detection of landslides with variable proportions using remote sensing images

Abstract

Efficient and automatic landslide detection solutions are beneficial for regional hazard mitigation. At present, scholars have carried out landslide detection based on deep learning. However, continuous improvement regarding the accuracy of landslide detection with better feature extraction of landslides remain an essential issue, especially small-proportion landslides in the remote sensing images are difficult to identify up to date. To address this issue, we propose a detection model, the so-called Dynahead-Yolo which is designed by combining unifying scale-aware, space-aware, and task-aware attention mechanisms into the YOLOv3 framework. The proposed method focuses on the detailed features of landslide images with variable proportions, improving the ability to decode landslides in complex background environments. We determine the most efficient cascade order of the three modules and compare previous detection networks based on randomly generated prediction sets from the three study areas. Compared with the traditional YOLOv3, the detection rate of Dynahead-Yolo in small-proportion landslides and complex background landslides is increased by 13.67% and 14.12%, respectively.