Underwater Engineering Crack Identification based on Lightweight Convolutional Neural Network

Convolutional neural network is an effective model for image feature recognition, which is widely used in crack recognition and risk detection. The crack identification of underwater engineering involves a huge scale of data, and the traditional R-CNN model and Faster R-CNN model have the problems of large number of model parameters and low efficiency for crack identification. Therefore, a lightweight convolutional neural network based crack identification method for underwater engineering is proposed to optimize the efficiency and accuracy of crack identification. Deep separable convolution is used to replace the common convolution of YOLOX network (an open source high-performance object detection algorithm), reducing the redundant parameters of the model to achieve lightweight design. Following the backbone feature extraction network framework of YOLOX, Transformer (a structure based on attention mechanism and forward neural network) vision module is adopted to replace the CSP structure (a feature extraction structure) at the end of the backbone network, adding attention mechanism and enhancing key information extraction capability. Make up for the problem of decreasing feature extraction accuracy caused by decreasing parameter number; Finally, the adaptive spatial feature fusion strategy is used instead of the traditional feature pyramid cascade to implement feature fusion and improve the feature extraction ability of small targets. The test results show that the improved YOLOX lightweight convolutional neural network has good prediction ability for underwater engineering cracks, and can accurately select the crack location, with fewer false detection and missed detection cases. Ablation experiments show that this study builds a lightweight convolutional neural network model, which improves the accuracy of the model to identify underwater engineering cracks. The improved lightweight convolutional neural network is suitable for crack identification in underwater engineering and can be widely applied in the field of underwater engineering risk identification.