摘要：Unmanned aerial vehicle (UAV)-based visual inspection is frequently employed for surface defect recognition. However, the recognition accuracy of UAVs is diminished by the presence of background interference and the small size of defects. To address these challenges, this paper introduces a novel framework that comprises an online image preprocessing module and the Pipe-MobileNet neural-network-based model. The preprocessing module aims to generate images without background interference, while the Pipe-MobileNet model incorporates a customized depthwise convolution operator that classifies convolution kernels, making it more efficient in defect classification. To validate the effectiveness of the proposed method, a series of experiments was conducted on two realistic DN100 and DN200 pipelines. These results underscore the method's marked improvements in recognition accuracy and computational efficiency.

关键字：pipeline defects inspection; unmanned aerial vehicle; image denoising; background subtraction; defect recognition

ISSN号：0957-0233

卷、期、页：卷36期1

发表日期：2025-01-31

影响因子：0.000000

期刊分区（SCI为中科院分区）：三区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称：MEASUREMENT SCIENCE AND TECHNOLOGY

通讯作者：马英涵,苗兴园,高博轩,宋福霖

第一作者：赵弘

论文类型：期刊论文

论文概要：马英涵,赵弘,苗兴园,高博轩,宋福霖，A UAV-based framework for quick recognition of pipeline defects，MEASUREMENT SCIENCE AND TECHNOLOGY，2025，卷36期1

论文题目：A UAV-based framework for quick recognition of pipeline defects