Imaging-based crack detection on concrete surfaces using You Only Look Once network

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Abstract

The detection of cracks in concrete structures is a pivotal aspect in assessing structural robustness. Current inspection methods are subjective, relying on the inspector’s experience and mental focus. In this study, an ad hoc You Only Look Once version 2 object detector was applied to automatically detect concrete cracks from real-world images, which were taken from diverse concrete bridges and contaminated with handwriting scripts. A total of 3010 cropped images were used to generate the dataset, labelled for two different detection classes, that is, cracks and handwriting. The proposed network was then trained and tested using the generated image dataset. Three full-scale images that contained disturbing background information were used to evaluate the robustness of the trained detector. The influence of labelling handwriting as an object class for network training on the overall crack detection accuracy was assessed as well. The results of this study show that the You Only Look Once version 2 could automatically locate crack with bounding boxes from raw images, even with the presence of handwriting scripts. As a comparative study, the proposed network was also compared with faster region-based convolutional neural network. The results showed that You Only Look Once version 2 performed better in terms of both accuracy and inference speed.

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Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition.

Kaiming He, Xiangyu Zhang, Shaoqing Ren … (2015)

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 × 224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102 × faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.