Faster multi-defect detection system in shield tunnel using combination of FCN and faster RCNN. Issue 13 (October 2019)

• Record Type: Journal Article
• Authors: Gao, Xinwen; Jian, Ming; Hu, Min; Tanniru, Mohan; Li, Shuaiqing
• Is Part Of: Advances in structural engineering. Volume 22:Issue 13(2019)
• Page Start: 2907
• Page End: 2921
• Publication Date: 2019-10
• Subjects: cylindrical projection -- deep learning -- faster RCNN -- field of view conversion -- FCN -- multi-defect of tunnel detection -- ROI; Structural engineering -- Periodicals; Construction, Technique de la; Structural engineering; Periodicals; 624.1
• DOI: 10.1177/1369433219849829
• Languages: English
• ISSNs: 1369-4332

Abstract

With the large-scale construction of urban subways, the detection of tunnel defects becomes particularly important. Due to the complexity of tunnel environment, it is difficult for traditional tunnel defect detection algorithms to detect such defects quickly and accurately. This article presents a deep learning FCN-RCNN model that can detect multiple tunnel defects quickly and accurately. The algorithm uses a Faster RCNN algorithm, Adaptive Border ROI boundary layer and a three-layer structure of the FCN algorithm. The Adaptive Border ROI boundary layer is used to reduce data set redundancy and difficulties in identifying interference during data set creation. The algorithm is compared with single FCN algorithm with no Adaptive Border ROI for different defect types. The results show that our defect detection algorithm not only addresses interference due to segment patching, pipeline smears and obstruction but also the false detection rate decreases from 0.371, 0.285, 0.307 to 0.0502, respectively. Finally, corrected by cylindrical projection model, the false detection rate is further reduced from 0.0502 to 0.0190 and the identification accuracy of water leakage defects is improved.