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Surface damage detection for concrete bridges using single-stage convolutional neural networks
Author(s): Chaobo Zhang; Chih-Chen Chang
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Paper Abstract

Detecting surface damages is vital for keeping concrete bridges structurally healthy and reliable. Currently, most of imagebased detection techniques are based on handcrafted low-level features which make them less applicable to actual images taken under varying environmental conditions. Recent rapid advancement in convolution neural network has enabled the development of deep learning-based visual inspection techniques for detecting multiple structural damages without needing manually-crafted features. However, most deep learning-based techniques are built on two-stage, proposal-driven detectors using less complex image data, which is not promising for practical applications and integration within intelligent autonomous inspection systems. In this study, a faster, simpler single-stage detector is proposed based on the real time object detection technique, You Only Look Once (YOLOv3) for detecting multiple surface damages of concrete bridges. A large field inspection images dataset of bridge damage is used for training and testing of YOLOv3. The original YOLOv3 is further improved by introducing a novel transfer learning method, batch renormalization and focal loss. The results show that the improved YOLOv3 has a detection accuracy of up to 80% and its performance is about 13% better than the original YOLOv3.

Paper Details

Date Published: 1 April 2019
PDF: 8 pages
Proc. SPIE 10972, Health Monitoring of Structural and Biological Systems XIII, 109722E (1 April 2019); doi: 10.1117/12.2513571
Show Author Affiliations
Chaobo Zhang, Hong Kong Univ. of Science and Technology (Hong Kong, China)
Chih-Chen Chang, Hong Kong Univ. of Science and Technology (Hong Kong, China)

Published in SPIE Proceedings Vol. 10972:
Health Monitoring of Structural and Biological Systems XIII
Paul Fromme, Editor(s)

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