Two-stage method based on the you only look once framework and image segmentation for crack detection in concrete structures

Abstract

Detecting the presence of cracks and identifying their severity are crucial tasks for determining the structural health of a concrete building. In this study, we develop a two-stage automated method based on the You Only Look Once (YOLOv5) deep learning framework for the identification, localization, and quantification of cracks in the concrete structures. In the first stage, cracks are identified and localized using bounding boxes, while in the second stage, the length of cracks and, therefore, the damage severity are determined. The developed deep learning model is trained using 4500 annotated images from a total of 40000 images of size 227 × 227 pixel, which are obtained from an open-source dataset collected at various campus buildings of Middle East Technical University (METU). The concept of transfer learning (i.e., pre-trained weights) is used for the training, which drastically reduces the training time. The detection and localization accuracy of this model is measured in terms of the average precision, average recall, and F1-score. The YOLOv5 model achieves the mean average precision (mAP_0.5) of 95.02%. A ResNet model is also developed just to capture the supremacy of the YOLOv5 model. The proposed method can help in identifying structural anomalies through real-time monitoring that must be urgently repaired and thus can be used in high-quality civil infrastructure monitoring systems.

Localization and Classification of Venusian Volcanoes Using Image Detection Algorithms

“…The backbone layer is mainly used to extract important features from a given input image. The backbone of the YOLOv5 architecture is called CSPDarknet-53, and it contains 23 residual units [ 25 ]. Residual units are made from one CNN layer with one 3 × 3 layer and one 1 × 1 layer.…”

“…The last part of the YOLOv5 structure is the head. The head uses a feature pyramid network (FPN) to detect objects at three different scales [ 25 ]. In short, the head layer generates three feature maps of different sizes that are used for multi-scale prediction effectively allowing a model to find large to small-sized objects on images.…”

Deep Learning for Concrete Crack Detection and Measurement

“…Kim et al [16] measured concrete crack width in pixels by using binarisation. ; Ioli et al [14] used the medial axis transform to measure crack width on crack masks that were obtained by using the Canny edge detection image processing algorithm [12]; Yang et al [17] used medial axis transform to measure crack width in pixels after performing skeletonization; and Mishra et al [35] measured crack width in pixels by first measuring the length of the crack and the area of the segmented crack, before calculating average crack width by dividing the area of the crack by the length of the crack.…”