Research on Rapid Detection and Crack Recognition Algorithm for Highway Pavement Based on Feature Space

Road surface detection is an important indicator for evaluating road conditions. Once defects appear on the road surface, it can lead to disasters in road traffic. Common road surface detection methods rely on manual visual inspection, which has problems such as low recognition accuracy and difficulty in identifying potential risks. In recent years, with the continuous development of visual technology, the application of image recognition technology in road surface detection has emerged. In order to improve the accuracy and efficiency of crack recognition on highways, a rapid detection and crack recognition algorithm based on feature space is proposed. This algorithm improves on the existing KPCA algorithm by introducing a decentralized matrix, thereby reducing the complexity of image processing. Simulation results show that the improved algorithm achieves an accuracy rate exceeding 95.5%, with an execution efficiency within 23.739 seconds, and the system memory usage is relatively stable. The application of this algorithm provides new technical means for rapid detection and crack recognition of highway pavement.