Research on Bridge Crack Identification Based on Convolutional Neural Networks

To address the issue of complex image conditions on bridge surfaces in intelligent bridge crack detection scenarios, which severely interfere with the recognition effectiveness of intelligent recognition algorithms, a bridge crack recognition algorithm combining "classification and screening + crack recognition" is proposed. The MobilenetV3 network structure is adjusted based on the image characteristics of actual bridge inspection photographs to construct a screening and classification model for bridge surface images, enabling automatic image filtering. A crack recognition model is trained through transfer learning to achieve crack recognition and localization, paving the way for further fine segmentation and measurement of cracks. During the algorithm development process, an image classification dataset containing 5,000 images was constructed, and the highest image classification accuracy achieved was 97.8%. The results indicate that the intelligent bridge crack detection method proposed in this paper can achieve efficient and automated detection of bridge cracks. The algorithm demonstrates strong anti-interference capabilities and basically meets practical application requirements.