基于无人机和深度学习的大型垃圾填埋场覆膜病害智能识别方法研究
Research on Intelligent Identification of Film Covering Diseases in Large Landfill Based on UAV and Deep Learning
宋树祥 1齐添 1胡良军 2张效刚 1陈彬荣 1张玉飞1
作者信息
- 1. 广州环投环境服务有限公司,广东 广州 510540
- 2. 广州市市政工程试验检测有限公司,广东广州 510520;清华大学博士后流动站,北京 100084
- 折叠
摘要
为提高大型生活垃圾卫生填埋场的堆体覆膜穿刺、撕裂等常见病害的巡检效率,以广州市兴丰生活垃圾卫生填埋场为研究对象,基于无人机航拍和深度学习模型,考察了无人机的选型和巡航参数,使用无人机采集病害样本并在病害图像识别模型中采用过采样的训练策略.并在YOLOv5 模型上增加细微目标识别层,可实现较高的识别准确率与召回率.研究结果表明:在病害图库样本数量相对有限的前提下利用过采样的策略提升了样本的代表性与均衡性,显著提高了模型病害识别的准确率.无人机结合高精度RTK定位技术能准确定位航拍照片坐标,解决了无明显参照物下覆膜病害定位困难的问题.通过查询病害缺陷照片的地理信息,可以快速找到病害位置,有助于及时开展修复作业.
Abstract
In order to improve the inspection efficiency of large domestic waste sanitary landfill for diseases such as puncturing and tearing of the covering film,the Xingfeng landfill in Guangzhou was taken as an example.The selection and cruise parameters of unmanned aerial vehicles(UAV)were investigated based on UAV aerial photography and depth learning model,the disease samples collected by UAV were trained by oversampling strategy in the disease image recognition model.A subtle target recognition layer was added to the YOLOv5 model,which achieved high recognition accuracy and recall rate.The test results showed that the oversampling strategy improved the representativeness and balance of the samples and significantly improved the accuracy of disease identification of the model under the condition that the number of samples in the disease map database was relatively limited.UAV combined with high-precision RTK positioning technology could accurately locate the coordinates of aerial photos,solved the problem of difficult positioning of film coating diseases without obvious reference objects.By querying the geographic information of disease defect photos,the location of the disease could be quickly found and repair operations could be carried out in a timely manner.
关键词
填埋场/HDPE膜/巡检/病害识别/迁移学习/无人机Key words
landfill/HDPE film/patrol inspection/disease identification/transfer learning/unmanned aerial vehicles引用本文复制引用
基金项目
广东省住房和城乡建设厅科学技术计划项目(2023-R24-434173)
广州市建筑集团有限公司科技计划项目([2023]-KJ013)
广州市建筑集团有限公司科技计划项目([2022]-KJ022)
广州环投环境服务有限公司科技项目(KYXMHF-2022-007)
出版年
2024
NETL
NSTL
维普
万方数据