Improved Mask R-CNN with distance guided intersection over union for GPR signature detection and segmentation
发布时间：2021-06-28
点击次数：
影响因子：6.121
DOI码：10.1016/j.autcon.2020.103414
发表刊物：Automation in Construction
关键字：Ground penetrating radar (GPR) ; Deep learning (DL) ; Civil infrastructure ; Mask R-CNN ; Detection and segmentation ; Intersection over union (IoU).
摘要：Ground penetrating radar (GPR) has been used for non-destructive inspection of civil infrastructure systems such as bridges and pipelines. Manually extracting useful data from a large amount of non-intuitive GPR scans is tedious and error-prone. To address this challenge, a generalizable end-to-end framework is developed and implemented to simultaneously detect and segment object signatures in GPR scans. The proposed approach improves the Mask Region-based Convolutional Neural Network (R-CNN) by incorporating a novel distance guided intersection over union (DGIoU) as a new loss function for detection and segmentation. The DGIoU considers the center distance between two bounding boxes and overcomes the weakness of intersection over union (IoU) in training and evaluation. In addition, a new method is proposed to extract data points from the segmented mask patches containing both object signatures and background noises. The extracted data points can be further processed for object localization and characterization. Experiments were conducted using GPR scans collected from a concrete bridge deck. The hyperbolic signatures of rebars can be accurately detected and segmented using the proposed method. It was demonstrated that using DGIoU improves the regression effect of bounding box and mask. The improved Mask R-CNN achieved an average accuracy (AP) of 58.64% and 47.64% for the detection and segmentation task, respectively.
第一作者：侯斐斐(博士生)
论文类型：期刊论文
通讯作者：雷文太
学科门类：工学
一级学科：信息与通信工程
文献类型：J
卷号：121
期号：2021
页面范围：1-14
ISSN号：0926-5805
是否译文：否
发表时间：2020-10-03
收录刊物：SCI、EI