Received: MaAccepted: SeptemPublished: October 4, 2022Ĭopyright: © 2022 Lu et al. PLoS ONE 17(10):Įditor: Ardashir Mohammadzadeh, University of Bonab, ISLAMIC REPUBLIC OF IRAN The experimental results demonstrated that ISSD is effective in bridge crack detection tasks and offers competitive performance compared to state-of-the-art networks.Ĭitation: Lu G, He X, Wang Q, Shao F, Wang J, Jiang Q (2022) Bridge crack detection based on improved single shot multi-box detector. 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.
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