Detection and classification of pipe defects based on pipe-extended feature pyramid network

棱锥(几何) 特征(语言学) 管道(软件) 计算机视觉 人工智能 采样(信号处理) 卷积神经网络 模式识别(心理学) 特征提取 计算机科学 瓶颈 边缘检测 目标检测 图像(数学) 图像处理 数学 几何学 哲学 语言学 滤波器(信号处理) 嵌入式系统 程序设计语言
作者
Wenhao Guo,Xing Zhang,Dejin Zhang,Zhipeng Chen,Baoding Zhou,Dingfa Huang,Qingquan Li
出处
期刊:Automation in Construction [Elsevier BV]
卷期号:141: 104399-104399 被引量:6
标识
DOI:10.1016/j.autcon.2022.104399
摘要

In image-based pipe defect detection research, the effective utilization of the information in the two-dimension (2D) image is directly related to the sampling of the image. The existing inspection methods do not analyze the pipeline imaging but rather directly use the object detection method for defect detection, resulting in a bottleneck problem for the accuracy. In this study, the pipeline imaging was analyzed. It was found that effective sampling of the defect texture within the edge region of the image could improve defect detection accuracy. An image sampling framework, pipe-extended feature pyramid network (P-EFPN), was constructed, and the super-resolution (SR) module was embedded for texture extraction to obtain rich defect texture information and provide image sampling support for pipe defect detection. The defect dataset contains deformation, corrosion, and crack. In the faster region-convolutional neural network (R-CNN) model with Resnet-101 as the backbone, the mean average precision (mAP) of the P-EFPN model was improved by 8.64% compared to the state-of-the-art feature pyramid network (FPN) model. The proposed method improves the accuracy of defect detection by capturing more textures in the edge regions of the image. Compared with existing image sampling methods, the proposed sampling method is more suitable for pipe defect detection.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
刚刚
1秒前
乐乐应助机智谷蕊采纳,获得10
1秒前
4秒前
5秒前
6秒前
8秒前
9秒前
小马过河应助尼尼采纳,获得10
11秒前
11秒前
11秒前
吾将上下而求索完成签到,获得积分10
11秒前
11秒前
11秒前
科研通AI2S应助LIN采纳,获得10
12秒前
12秒前
12秒前
喜悦的半青完成签到,获得积分10
12秒前
13秒前
好宝宝发布了新的文献求助10
14秒前
上官若男应助程艳采纳,获得80
14秒前
伊可创发布了新的文献求助10
15秒前
Ava应助szh123采纳,获得10
16秒前
锦七发布了新的文献求助10
16秒前
小二郎应助收手吧大哥采纳,获得10
18秒前
19秒前
在水一方应助lm采纳,获得10
19秒前
可爱的函函应助jingjingA采纳,获得10
19秒前
Zdh同学完成签到,获得积分10
20秒前
我是老大应助淡然的铭采纳,获得10
21秒前
girl完成签到,获得积分10
22秒前
23秒前
华仔应助HAHAHA采纳,获得10
23秒前
23秒前
小坤同学发布了新的文献求助10
24秒前
25秒前
musejie应助科研通管家采纳,获得10
25秒前
科研通AI2S应助科研通管家采纳,获得10
25秒前
quhayley应助科研通管家采纳,获得10
25秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988732
求助须知:如何正确求助?哪些是违规求助? 3531027
关于积分的说明 11252281
捐赠科研通 3269732
什么是DOI,文献DOI怎么找? 1804764
邀请新用户注册赠送积分活动 881869
科研通“疑难数据库(出版商)”最低求助积分说明 809021