Engineering-oriented bridge multiple-damage detection with damage integrity using modified faster region-based convolutional neural network

被引：14

作者：

Yu, Licun ^{[1
,2
]}

He, Shuanhai ^{[1
]}

Liu, Xiaosong ^{[3
]}

Ma, Ming ^{[2
]}

Xiang, Shuiying ^{[3
]}

机构：

[1] Changan Univ, Sch Highway, Xian 710064, Peoples R China

[2] CCCC First Highway Consultants Co Ltd, Xian 710075, Peoples R China

[3] Xidian Univ, State Key Lab Integrated Serv Networks, Xian 710071, Peoples R China

来源：

MULTIMEDIA TOOLS AND APPLICATIONS | 2022年 / 81卷 / 13期

基金：

中国国家自然科学基金;

关键词：

Bridge inspection; Deep learning; Faster region-based convolutional neural network (faster R-CNN); Multiple-damage detection; CRACK DETECTION; DEFECT DETECTION; MACHINE VISION; DEEP; INSPECTION;

D O I：

10.1007/s11042-022-12703-8

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A bridge damage detector with preserving integrity based on modified Faster region-based convolutional neural network (R-CNN) is proposed for multiple damage types. The methodologies of dataset collection, damage annotation, and anchors generation are modified. The performance for bridge multiple-damage detectors with ResNet50 or ResNet101 as feature extraction network are compared. The results show that, with the modified Faster R-CNN, the mean average precision reaches 84.56% (76.43%) at the intersection-over-union metrics of 0.5 (0.75). We further demonstrate that the localization offset for Faster R-CNN is lower than that of YOLOv3. The modified bridge damage detector enables better detecting performance, and can preserve the damage integrity.

引用

页码：18279 / 18304

页数：26

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