Detection of geohazards caused by human disturbance activities based on convolutional neural networks
Heng ZHANG , Diandian ZHANG , Da YUAN , Tao LIU
Water Resources and Hydropower Engineering ›› 2025, Vol. 56 ›› Issue (S1) : 731 -738.
Human disturbance activities is one of the main reasons for inducing geohazards. Ecological impact assessment metrics of roads are inconsistent criteria and multiple. From the perspective of visual observation, the environment damage can be shown through detecting the uncovered area of vegetation in the images along road. To realize this, an end-to-end environment damage detection model based on convolutional neural network is proposed. A 50-layer residual network is used to extract feature map. The initial parameters are optimized by transfer learning. An example is shown by this method. The dataset including cliff and landslide damage are collected by us along road in Shennongjia national forest park. Results show 0. 470 3 average precision(AP) rating for cliff damage and 0. 480 9 average precision(AP) rating for landslide damage. Compared with YOLOv3, our model shows a better accuracy in cliff and landslide detection although a certain amount of speed is sacrificed.
convolutional neural network / detection / environment damage / cliff / landslide
Hubei Province Postdoctoral Innovative Practice Position “Construction simulation and support optimization of hydraulic tunnel based on bonded block-synthetic rock mass method”(2023CXGW01)
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