Classification of Pavement Distress Images Using Fusion Convolutional Neural Network of Dual Branch

In recent years, the issue of highway maintenance has become increasingly prominent. How to precisely detect and classify fine cracks and various types of pavement defects on highways through technical means is an essential foundation for achieving intelligent road maintenance. This paper first constructs the DenseNet201-PDC and MobileNetV2-PDC sub-classification networks that incorporate the three-channel attention judgment mechanism MCA. Secondly, based on the principle of parallel connection, a brand-new dual-branch fusion convolutional neural network DBF-PDC capable of classifying pavement defects in highway scenarios is proposed. Finally, this paper builds the Pavement Distress Datasets of Southeast University and conducts relevant ablation experiments. The experimental results demonstrate that both the attention mechanism module and the feature fusion strategy can significantly enhance the network's ability to classify pavement defects in highway scenarios. The average classification accuracy of the five types of defects in the proposed DBF-PDC network reaches 99.43%, and the real-time performance is 55.47 ms per frame.