Surface damage detection for concrete bridges using single-stage convolutional neural networks

Chaobo Zhang; Chih-Chen Chang

doi:10.1117/12.2513571

1 April 2019 Surface damage detection for concrete bridges using single-stage convolutional neural networks

Chaobo Zhang, Chih-Chen Chang

Proceedings Volume 10972, Health Monitoring of Structural and Biological Systems XIII; 109722E (2019) https://doi.org/10.1117/12.2513571
Event: SPIE Smart Structures + Nondestructive Evaluation, 2019, Denver, Colorado, United States

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.

Citation Download Citation

Chaobo Zhang and Chih-Chen Chang "Surface damage detection for concrete bridges using single-stage convolutional neural networks", Proc. SPIE 10972, Health Monitoring of Structural and Biological Systems XIII, 109722E (1 April 2019); https://doi.org/10.1117/12.2513571

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