The maintenance of road infrastructure is crucial for ensuring safe and efficient transportation systems. However, traditional manual inspection methods for identifying road defects, such as cracks and potholes, are time-consuming, resource-intensive, and prone to errors. To address this challenge, the objective of this project is to develop an AI-driven system for automated road defect detection and maintenance. By leveraging computer vision and machine learning, the aim is to train a model capable of accurately detecting and classifying various types of road defects from image or video data.
Implementing this AI-powered system will streamline road inspection processes, enabling efficient identification of road defects and prioritization of maintenance efforts
This project aims to investigate the ability of various machine learning classifiers to detect road defects with the highest possible accuracy and build a dashboard to visualize detected road defects. The goal is to automate the inspection process to reduce time and effort for better efficiency.
– Data Pre-Processing
– Prepare/ Explore ML approaches
– Explore the pre-trained network and ML models
– Classifier training and validation Performing transfer learning
– System testing and accuracy reporting 4.2 Building Dashboard to visualize the output/ Deployment
– Generate final report and recommendation/ Evaluate Model accuracy
– EDA (Exploratory Data Analysis), Image Classfier, AI. Data Visualization. Python