Traditional manual detection is not only time-consuming and laborious but also has many unsafe factors. The development of bridge crack-detection methods has been relatively slow.

To maintain the healthy state of bridges, it is important for the engineering community, national government administrative services, and bridge construction companies to detect and repair cracks in a timely manner. Furthermore, the presence of cracks affects the integrity, durability, and seismic performance of a bridge and considerably reduces its quality. Cracks in a bridge accelerate the speed of corrosion of the armature, resulting in deterioration of the bridge structure. Among them, deck cracks are a common problem in bridge services. However, bridges are prone to various types of damage owing to natural or human factors. The experimental results demonstrated that ISSD is effective in bridge crack detection tasks and offers competitive performance compared to state-of-the-art networks.Īs a fundamental component of the transportation system, the bridge not only takes responsibility for transporting items but also ensures the safety of the transport personnel. The FRM was employed to determine the importance of each feature channel through learning, enhance the useful features according to their importance, and suppress the features that are insignificant for bridge crack detection. IM was designed to expand the width of the network, reduce network calculations, and improve network computing speed. Specifically, DSDCM was utilized for extracting the characteristic information of irregularly shaped bridge cracks. In this study, an improved single-shot multi-box detector (SSD) called ISSD is proposed, which seamlessly combines the depth separable deformation convolution module (DSDCM), inception module (IM), and feature recalibration module (FRM) in a tightly coupled manner to tackle the challenges of bridge crack detection. However, these networks have limited utility in bridge crack detection because of low precision and poor real-time performance. Owing to the development of computerized vision technology, object detection based on convolutional neural networks is being widely used in the field of bridge crack detection.

Multi extractor with crack