Comparative Analysis of Multiple Methods Utilized for Road Pothole Identification Using Deep Learning

The public’s safety that asphalt roads be repaired and maintained by deep learning techniques. It is important to determine the current road pavement condition before making any plans for preventative maintenance on a road or other infrastructure. Damaged road has cracks, potholes, lines, and shade. Within the scope of this research, we investigated several approaches to pothole detection and developed an effective modified MobileNetV2 (MMNV2) technique. In this study, a modified version of the MobileNetV2 model is proposed for use in feature extraction, picture classification, and detection using deep learning (DL) by using the transfer learning approach. A pretrained MNV2 model was given an additional five layers in order to make it work better overall and categorize normal things more accurately, and pothole images using this method, a Python model that was trained and tested using 2,000 images of road pavement was produced. When transfer learning was combined with a deep neural network (DNN) architecture, the outcomes of this investigation demonstrated that our MMNV2 method was successful. Timely detection and repair of defects can optimize resource allocation, leading to cost reductions in road maintenance and enhanced operational efficiency. Consequently, pothole identification is essential for protecting the safety of roads, enhancing the flow of traffic, preserving infrastructure, and making the most efficient use of available resources.