Vision-Based Asphalt Pavement Defect Detection Using Deep CNN with BIM Integration

Abstract

The major objective of the research is to predict the defects on the asphalt pavement which is the main concern for the smooth movement of vehicles by using deep CNN. CNN consists of different operations that is image classification, object detection, and image segmentation. We use detectron2 for labelling the defective part to detect the defect accurately. The data set which consists of images of defects of pavement is collected and used for training the algorithm and some of the collected images are used for the testing. The suggested approach makes use of deep learning to automatically detect and categorize a range of asphalt pavement defects, such as surface distress, potholes, and cracks. The training, validation, and testing of the model are conducted on an extensive dataset of annotated pavement photographs. The trained model shows resilience and the ability to generalize, which qualifies it for practical uses in infrastructure management and pavement upkeep. This study advances automated pavement inspection systems, which provide a practical and economical means of determining which maintenance tasks should be prioritized to maintain safer and smoother roads and this approach predicted the defects with an accuracy of 98%.