Automatic and Efficient Detection of Loess Landslides Based on Deep Learning

Qingyun Ji, Yuan Liang (), Fanglin Xie, Zhengbo Yu and Yanli Wang
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Qingyun Ji: College of Mathematics and Physics, Chengdu University of Technology, Chengdu 610059, China
Yuan Liang: Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059, China
Fanglin Xie: College of Resources and Environment, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Zhengbo Yu: College of Mathematics and Physics, Chengdu University of Technology, Chengdu 610059, China
Yanli Wang: College of Mathematics and Physics, Chengdu University of Technology, Chengdu 610059, China

Sustainability, 2024, vol. 16, issue 3, 1-20

Abstract: Frequent landslide disasters on the Loess Plateau in northwestern China have had a serious impact on the lives and production of the people in the region due to the fragile ecological environment and severe soil erosion. The effective monitoring and management of landslide hazards is hindered by the wide range of landslide features and scales in remotely sensed imagery, coupled with the shortage of local information and technology. To address this issue, we constructed a loess landslide dataset of 11,010 images and established a landslide detection network model. Coordinate Attention (CA) is integrated into the backbone with the aid of the YOLO model to capture precise location information and remote spatial interaction data from landslide images. Furthermore, the neck includes the Convolutional Block Attention Module (CBAM), which prompts the model to prioritize focusing on legitimate landslide objectives while also filtering out background noise to extract valid feature information. To efficiently extract classification and location details from landslide images, we introduce the lightweight Decoupled Head. This enhances detection accuracy for landslide objectives without excessively increasing model parameters. Furthermore, the utilization of the SIoU loss function improves angle perception for landslide detection algorithms and reduces the deviation between the predicted box and the ground truth box. The improved model achieves landslide object detection at multiple scales with a mAP of 92.28%, an improvement of 4.01% compared to the unimproved model.

Keywords: loess landslide; object detection; yolo model; coordinate attention; convolutional block attention module; lightweight decoupled head; SIoU loss (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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