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The crack detection of concrete bridges is an important link in the safety evaluation of bridge structures, and the rapid and accurate identification and detection of bridge cracks is a prerequisite for ensuring the safety and long-term stable use of bridges. To solve the incomplete crack detection and segmentation caused by the complex background and small proportion in the actual bridge crack images, this paper proposes a coarse–fine combined bridge crack detection method of "double detection + single segmentation" based on deep learning. To validate the effect and practicality of fine crack detection, images of old civil bridges and viaduct bridges against a complex background and images of a bridge crack against a simple background are used as datasets. You Only Look Once V5(x) (YOLOV5(x)) was preferred as the object detection network model (ODNM) to perform initial and fine detection of bridge cracks, respectively. Using U-Net as the optimal semantic segmentation network model (SSNM), the crack detection results are accurately segmented for fine crack detection. The test results showed that the initial crack detection using YOLOV5(x) was more comprehensive and preserved the original shape of bridge cracks. Second, based on the initial detection, YOLOV5(x) was adopted for fine crack detection, which can determine the location and shape of cracks more carefully and accurately. Finally, the U-Net model was used to segment the accurately detected cracks and achieved a maximum accuracy (AC) value of 98.37%. The experiment verifies the effectiveness and accuracy of this method, which not only provides a faster and more accurate method for fine detection of bridge cracks but also provides technical support for future automated detection and preventive maintenance of bridge structures and has practical value for bridge crack detection engineering. [ABSTRACT FROM AUTHOR]

Cracking is one of the most common bridge diseases. If bridge cracks are not repaired: in time, they may cause gradual changes to the concrete structure, which can seriously affect its strength. A network called YOLOv5-TS is what we suggest to detect intelligently bridge surface cracks in images. To improve the network performance, we integrate SPPCSPC into the original YOLOv5 network to ensure adaptive image output and obtain receptive fields of various sizes. Meanwhile, transposed convolution is incorporated to improve the capacity of the network for learning weights on its own and reduce characteristic information loss. In response to the diverse morphology of bridge cracks, cracks are identified according to their mechanical causes crack inclination, and divided into four categories: horizontal cracks (0 ∘ –20 ∘ ), low-angle cracks (20 ∘ –45 ∘ ), vertical cracks (70 ∘ –90 ∘ ) and high-angle cracks (45 ∘ –70 ∘ ). Experiments on the ZJU SYG crack data set confirm that the proposed YOLOv5-TS has a better crack intelligent identification effect on bridge surface images than other compared baselines. The best performance of YOLOv5-TS is found in mAP@0.5 (0.752), mAP@0.5:0.95 (0.518), and recall (0.794), thus demonstrating the model's practical value. [ABSTRACT FROM AUTHOR]

The crack detection of concrete bridges is an important link in the safety evaluation of bridge structures, and the rapid and accurate identification and detection of bridge cracks is a prerequisite for ensuring the safety and long-term stable use of bridges. To solve the incomplete crack detection and segmentation caused by the complex background and small proportion in the actual bridge crack images, this paper proposes a coarse–fine combined bridge crack detection method of “double detection + single segmentation” based on deep learning. To validate the effect and practicality of fine crack detection, images of old civil bridges and viaduct bridges against a complex background and images of a bridge crack against a simple background are used as datasets. You Only Look Once V5(x) (YOLOV5(x)) was preferred as the object detection network model (ODNM) to perform initial and fine detection of bridge cracks, respectively. Using U-Net as the optimal semantic segmentation network model (SSNM), the crack detection results are accurately segmented for fine crack detection. The test results showed that the initial crack detection using YOLOV5(x) was more comprehensive and preserved the original shape of bridge cracks. Second, based on the initial detection, YOLOV5(x) was adopted for fine crack detection, which can determine the location and shape of cracks more carefully and accurately. Finally, the U-Net model was used to segment the accurately detected cracks and achieved a maximum accuracy (AC) value of 98.37%. The experiment verifies the effectiveness and accuracy of this method, which not only provides a faster and more accurate method for fine detection of bridge cracks but also provides technical support for future automated detection and preventive maintenance of bridge structures and has practical value for bridge crack detection engineering.

Infrastructure for transportation relies heavily on concrete bridges, therefore maintaining their health is essential for everyone's safety. A comparison of deep-learning algorithms for spotting cracks in concrete bridges is presented in this work. The proposed models include Convolutional Neural Network (CNN), Convolutional Recurrent Neural Network (CRNN), You Only Look Once (YOLO) versions YOLOv5, YOLOv7, YOLOv8, and Residual Networks (ResNet) leverage cutting-edge deep learning architectures and feature engineering techniques, enabling more precise crack detection in concrete bridge structures. To boost model generalization and the capacity to spot cracks in a variety of real-world scenarios, various data augmentation techniques, such as Gaussian blur, mix-up, random rotation, center crop, random crop, Gaussian noise, random blocks, central region, and smart padding, were also included. The studies utilized cracked and uncracked concrete bridge surface photos from the open-source SDNET dataset. The accuracy, precision, recall, and F1 score of each model are evaluated. YOLOv8 has the highest accuracy of 95%, whereas CNN and YOLOv5 showed poor performance. [ABSTRACT FROM AUTHOR]