河北工业科技2025，Vol.42Issue(4)：323-332,10.DOI:10.7535/hbgykj.2025yx04003
基于改进Mask RCNN的遥感影像滑坡识别方法研究
Research on landslide detection in remote sensing images based on improved Mask RCNN
摘要
Abstract
To improve the landslide detection accuracy in remote sensing images under complex backgrounds,a landslide detection method for remote sensing images based on an improved mask region-based convolutional network(Mask RCNN)was proposed.First,the backbone network in the Mask RCNN model was replaced with a residual network 101(ResNet101),and additional modules including the convolutional block attention module(CBAM),the path aggregation feature pyramid network(PAFPN),and a cascade detector were integrated to construct a landslide detection model based on remote sensing images;Then,the model was trained using a remote sensing landslide dataset;Finally,the trained model was used to perform detection and segmentation experiments on the test images.The results show that,compared with the original Mask RCNN model,the improved model increases the box average precision from 80.2%to 83.7%,and the mask average precision from 79.1%to 81.1%.The overall inference time remains virtually unchanged.The improved Mask RCNN model exhibits high detection accuracy and real-time processing capability,serving as a key technical support for landslide disaster early warning.关键词
计算机图像处理/滑坡识别/Mask RCNN/遥感影像/卷积块注意力模块Key words
computer image processing/landslide detection/Mask RCNN/remote sensing images/convolutional block atten-tion module分类
信息技术与安全科学引用本文复制引用
王建霞,郭玉凤,杨春金,张晓明..基于改进Mask RCNN的遥感影像滑坡识别方法研究[J].河北工业科技,2025,42(4):323-332,10.基金项目
河北省自然科学基金(F2022208002) （F2022208002）
