Smart Solutions for Road Defect Detection: A Deep Learning Fusion Approach with CNNs and SVMs

The purpose of this research is to develop a novel approach to identifying road issues by using a deep learning framework that combines support vector machines (SVMs) with convolutional neural network networks (CNNs). For seven different types of road faults, including transverse cracking, raveling, rutting, alligator cracking, reflection cracking, and potholes, we evaluate the performance of the model. Our model achieves outstanding accuracy, recall, and Fl-Score values as a result of diligent investigation, and strong discriminating capacity is shown by each defect class. The fact that our model consistently achieves a score of 0.99 across the board attests to its high level of accuracy. Integration of CNNs and SVMs enables our framework to identify and classify road defects, which in turn opens the door to enhanced transportation infrastructure management. This capability is made possible by the integration of CNNs and SVMs. A comprehensive assessment of the model's efficacy is also provided by our study, which includes the presentation of a variety of average metrics, including macro, weighted, as well as micro averages, among others. These findings assist in the development of automated transportation systems by drawing attention to the possibility of deep learning technologies to revolutionize the methods by which road faults are identified.