RESEARCH ON TWO-STAGE IDENTIFICATION ALGORITHM OF BRIDGE CRACKS BASED ON CONVOLUTIONAL NEURAL NETWORK

The complex surface image conditions of bridges seriously reduce the crack recognition accuracy of the intelligent identification algorithm. To address this issue, a two-stage bridge crack identification algorithm is proposed. The network structure of MobilenetV3 is adjusted according to the characteristics of bridge surface images, and a classification model for bridge surface images is constructed to achieve automatic filtering of background images, interfering images and low-quality images. A box-grid fusion crack identification model is trained to achieve crack locating, which is used for further crack segmentation and crack measurement. The final image classification accuracy is 97.9%, and the final crack recognition accuracy for grid prediction is 65.6%. The results indicate that the intelligent detection method for bridge cracks proposed in this article can achieve efficient and automated detection of bridge cracks, and the algorithm has strong anti-interference ability.