Automatic detection of potholes using VGG-16 pre-trained network and Convolutional Neural Network

Area of Science:

• Computer Science
• Artificial Intelligence
• Robotics

Background:

• Autonomous vehicles require advanced perception systems to navigate safely.
• Road hazards like potholes pose significant risks to vehicle integrity and passenger safety.
• Existing pothole detection methods have limitations including high costs and detection challenges.

Purpose of the Study:

• To develop an automated, accurate, and efficient system for real-time road pothole identification for autonomous vehicles.
• To leverage transfer learning and image enhancement techniques to overcome limitations of current methods.

Main Methods:

• Implemented a Convolutional Neural Network (CNN) utilizing the VGG-16 pre-trained model for transfer learning.
• Employed a Super-Resolution Generative Adversarial Network (SRGAN) to improve image quality for better pothole detection.
• Trained and evaluated the model on road pothole classification tasks.

Main Results:

• Achieved a high classification accuracy of 97.3% for road potholes.
• Demonstrated superior performance compared to other deep learning algorithms in pothole detection.
• The integrated approach of transfer learning and SRGAN proved effective in enhancing detection accuracy.

Conclusions:

• The proposed transfer learning technique, combined with SRGAN, offers a robust solution for automated pothole detection.
• This method significantly enhances the safety and reliability of autonomous driving systems by addressing road hazards.
• The high accuracy achieved indicates strong potential for real-world implementation in intelligent transportation systems.