Automatic detection of defects in concrete structures based on deep learning

Abstract

The inspection of defects in concrete infrastructure is a costly and time-consuming task. Many image processing techniques (IPTs) have been proposed to replace manual inspection of defects. However, the extensively varying situations in real world (e.g., lighting and shadow changes) pose challenges to the wide adoption of IPTs. To solve these problems, we propose an automated detection method for concrete surface defects based on deep learning. Our model is a one-stage object detection network, which is composed of two parts: the backbone network EfficientNetB0 and the detector. EfficientNetB0 is formed by stacking multiple mobile inverted bottleneck convolution (MBConv) blocks, improving the feature extraction performance while reducing the number of parameters. The detector is inspired by feature pyramid network which extracts feature information from three scales, and fuses low-level features with high-level features via up-sampling operation to improve detection accuracy. The training and testing of the model are carried out on the concrete surface defects dataset. Results show 76.4% and 89.9% average precision (AP) for crack and exposed bars, respectively, with a mean AP of 83.2%.