Implementation of wavelet method and backpropagation neural network on road crack detection based on image processing

Abstract

Road crack detection is critical to road infrastructure maintenance, requiring sophisticated and accurate approaches. This research explores the utilization of a combination of Wavelet and Convolutional Neural Network (CNN) methods to improve efficiency and accuracy in detecting cracks in road images. The wavelet method was chosen for its capability to capture information at different scales, enabling improved feature extraction. Meanwhile, CNN was utilized to comprehend the spatial context and tackle image complexity. The research involves several stages, including data collection, pre-processing, decomposition using the Wavelet method, forming of the CNN architecture model, training, testing, and evaluating the result. The tested images involve three main types of cracks: alligator, linear, and images without cracks. The testing results show that the developed model is capable of classifying cracks with an F1-score of 0.96, recall of 0.96, and precision of 0.96. In real-time detection of road cracks, the testing obtained an F1-score of 0.84, recall of 0.92, and precision of 0.77. This research contributes to the advancement of road crack detection technology by leveraging the capabilities of Wavelet and CNN, enhancing the accuracy and efficiency of crack detection in road maintenance.