Research on the classification and localization method of cracks in highway bridges based on MAMBA network

As an important component of transportation facilities, bridges have an increasing demand for inspection and maintenance. However, traditional manual detection methods have many problems in terms of efficiency, accuracy, and safety, making it difficult to meet today’s fast and accurate detection requirements. This article proposes an algorithm for detecting apparent cracks in highway bridges based on MAMBA network and digital image processing technology, this method adopts the detection box form, which can effectively locate and qualitatively detect cracks in concrete bridges accurately. To verify the effectiveness of the model, this paper created a dataset of bridge crack images and used the dataset hyperparameter evolution to obtain default parameters as initial parameters for training. During the training process, we considered adding the CA attention mechanism and the CBAM attention mechanism respectively for the trial process. By comparing the training results, it was found that the model with CA attention mechanism can effectively capture smaller disease features, thus achieving better detection performance. This method has certain advantages in both speed and accuracy, making it more effective in detecting cracks on the bottom of bridges.