Efficient Road Crack Detection and Classification System Using Convolutional Neural Networks

Abstract

Road infrastructure maintenance is critical for ensuring safety and efficiency in transportation systems. Developing robust road crack detection systems has gained significant attention in this context. This research paper proposes a methodology leveraging Convolutional Neural Networks (CNNs) for segmenting and classifying road cracks. The methodology involves several key steps, including acquiring a diverse dataset comprising images from various crack segmentation sources. Preprocessing techniques such as resizing, normalization, and data augmentation are applied to standardize and enhance the dataset. Subsequently, the dataset is split into training, validation, and testing sets to facilitate model training and evaluation. The segmentation phase utilizes a CNN model to generate probability maps, which are then thresholded to obtain binary masks indicating crack presence. Following segmentation, a classification step categorizes detected cracks into predefined classes, leveraging the hierarchical features learned during segmentation. The CNN model is fine-tuned for this task, optimizing parameters through backpropagation. Evaluating the model's performance on the testing set ensures its effectiveness in real-world scenarios. By integrating segmentation and classification tasks within a unified CNN framework, the proposed methodology achieves accurate and efficient road crack detection, contributing to enhanced infrastructure maintenance and safety.