SE-YOLOv7 Landslide Detection Algorithm Based on Attention Mechanism and Improved Loss Function

Qing Liu, Tingting Wu (), Yahong Deng and Zhiheng Liu
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Qing Liu: School of Mechanical and Electrical Engineering, Huainan Normal University, Huainan 232038, China
Tingting Wu: Shaanxi Key Laboratory of Land Consolidation, Chang’an University, Xi’an 710054, China
Yahong Deng: School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China
Zhiheng Liu: School of Aerospace Science and Technology, Xidian University, Xi’an 710054, China

Land, 2023, vol. 12, issue 8, 1-19

Abstract: With the continuous development of computer vision technology, more and more landslide identification detection tasks have started to shift from manual visual interpretation to automatic computer identification, and automatic landslide detection methods based on remote sensing satellite images and deep learning have been gradually developed. However, most existing algorithms often have the problem of low precision and weak generalization in landslide detection. Based on the Google Earth Engine platform, this study selected landslide image data from 24 study areas in China and established the DN landslide sample dataset, which contains a total of 1440 landslide samples. The original YOLOv7 algorithm model was improved and optimized by applying the SE squeezed attention mechanism and VariFocal loss function to construct the SE-YOLOv7 model to realize the automatic detection of landslides in remote sensing images. The experimental results show that the mAP, Precision value, Recall value, and F1-Score of the improved SE-YOLOv7 model for landslide identification are 91.15%, 93.35%, 94.54%, and 93.94%, respectively. At the same time, through a field investigation and verification study in Qianyang County, Baoji City, Shaanxi Province, comparing the detection results of SE-YOLOv7, it is concluded that the improved SE-YOLOv7 can locate the landslide location more accurately, detect the landslide range more accurately, and have fewer missed detections. The research results show that the algorithm model has strong detection accuracy for many types of landslide image data, which provides a technical reference for future research on landslide detection based on remote sensing images.

Keywords: landslide detection; deep learning; attention mechanism; YOLOv7 (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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