Micro-concrete crack detection of underwater structures based on convolutional neural network

Micro-cracks are often generated on the concrete structures of long-distance water conveyance projects. Without early detection and timely maintenance, micro-cracks may expand and deteriorate continuously, leading to major structural failure and disastrous results. However, due to the complexity of the underwater environment, many vision-based methods for concrete crack detection cannot be directly applied to the interior surface of water conveyance structures. In view of this, this paper proposes a three-step method to automatically detect concrete micro-cracks of underwater structures during the operation period. First, underwater optical images were preprocessed by a series of algorithms such as global illumination balance, image color correction, and detail enhancement. Second, the preprocessed images were sliced to image patches, which are sent to a convolutional neural network for crack recognition and crack boundary localization. Finally, the image patches containing cracks were segmented by the Otsu algorithm to localize the cracks precisely. The proposed method can overcome issues such as uneven illumination, color distortion, and detail blurring, and can effectively detect and localize cracks in underwater optical images with low illumination, low signal-to-noise ratio and low contrast. The experimental results show that this method can achieve a true positive rate of 93.9% for crack classification, and the identification accuracy of the crack width can reach 0.2 mm.