# Coarse-Fine Combined Bridge Crack Detection Based on Deep Learning
###### Abstract  
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) (YOLOVS(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 YOLOVS(x) was more comprehensive and preserved the original shape of bridge cracks. Second, based on the initial detection, YOLOVS(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. bridge cracks; deep learning; fine detection; ODNM; SSNM +
Footnote †: journal: Medical  
which is easily adapted for continuous monitoring by installing sensors at positions where the bridges have cracked or are expected to crack [10; 11; 12], has a limited monitoring range and may be subject to omission and misjudgement. Image processing detection methods mainly use image greyscale information for threshold segmentation to segment cracks from the background. Alternatively, using methods such as morphology [13; 14], cracks in bridge images are detected by edge detection [15], and the recognition results are too dependent on the selection of some specific parameters, which cannot ensure the reliability and stability of the crack detection results. With the fast evolution of deep learning, various intelligent auxiliary bridge crack detection technologies have emerged one after another. For specific research topics, the selection of datasets to be used must meet certain criteria and pertinence [16]. For example, the image acquisition of the bridge surface is carried out by an unmanned aerial vehicle (UAV) or a handheld camera, as shown in Figure 1. In this way, detection efficiency can be improved while maintaining safety, and the size and location of bridge cracks can be more accurately recorded. Recently, some researchers have applied deep learning based on object detection [17; 18; 19; 20; 21; 22; 23], semantic segmentation [24; 25] and instance segmentation [26; 27] to automate bridge crack detection, and remarkable progress has been made. By learning the mapping in the image, cracks can be accurately and efficiently identified, extracted and segmented from the complex background, providing technical support for fast and accurate bridge crack detection. At the same time, for the interpretation of the experimental results, it is possible to highlight a specific defect type in a given image almost perfectly by adopting Class Activation Map (CAM) approaches within the available eXplainable Artificial Intelligence (XAI) techniques, which allow the observation of the active region [28].