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
收藏
页码：18279 / 18304
页数：26
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