Multiple Road Fissures Detection Using Deep Learning Algorithm

Road infrastructure is critical in transportation systems because it ensures the safe and efficient movement of people and goods. However, the deterioration of roads over time as a result of various factors such as weather and heavy traffic poses significant maintenance and safety challenges. Early and accurate detection of road damage is critical for timely repairs and accident prevention. This paper proposes a novel approach to detecting road damage using Convolutional Neural Networks (CNNs). CNNs have demonstrated remarkable success in a variety of computer vision tasks, making them an appealing option for automated road damage detection. The goal of this research is to use deep learning and computer vision techniques to create an efficient and accurate system for detecting road damage from images. Our methodology entails gathering a diverse dataset of road images with various types of damage, such as potholes, cracks, and road surface degradation. The dataset is pre-processed to improve image quality and annotated for training and evaluation. Using this dataset, a custom CNN architecture is designed and trained to recognize and classify various types of road damage. A separate validation dataset is used to evaluate the trained model's performance in terms of accuracy, precision, recall, and F1 score. Furthermore, we investigate the model's ability to generalize to previously unseen road damage scenarios by testing it on real-world images captured under varying conditions. Our CNN-based road damage detection system achieves high accuracy in identifying and classifying road damage types, according to the results. This system can be integrated into existing infrastructure management systems, allowing for cost-effective and timely road maintenance. Furthermore, it helps to improve road safety by identifying potential hazards before they cause accidents.