Optimization of the road bump and pothole detection technology using convolutional neural network

Abstract

In highway bridge engineering, it is essential to use modern image processing methods to effectively detect and classify road bumps and potholes, considering the unique characteristics of bridge surfaces. Accurate identification and handling of road surface irregularities are crucial for preserving the longevity and security of transportation infrastructure. The study proposes a Convolutional Neural Network for Highway Anomaly Detection (BCNN-HAD) that uses the “BridgeGuard-Vision” (BGV) method, a computer vision technology for highway bridges to increase the accuracy and efficiency of automated image processing for more accurate detection of road irregularities in highway surveillance. Through model training using datasets that imitate various environmental conditions frequently seen on highway bridges, the proposed approach obtains dependable characteristics from photos taken close to bridges, improving flexibility and accuracy. The training method considers variations in lighting, weather conditions, and bridge materials, ensuring the model performs well in various real-world situations. In addition, this work explores the combination of sensor fusion techniques, combining data from many sources such as bridge structural health monitoring systems, cameras, accelerometers, and Global Positioning System. This comprehensive method, represented by BGV-YOLOv5, aims to offer a complete understanding of the bridge surroundings, therefore helping to detect road irregularities and further developing the field of bridge health monitoring. Expected results involve developing a personalized and effective system for detecting road bumps and potholes to tackle specific difficulties in highway bridge situations. In addition, the project seeks to provide a structure for smart transportation systems in the field of bridge engineering. This project seeks to address the particular requirements of highway bridge engineers, improving road safety and infrastructure maintenance methods in highway bridge engineering with the overall objective of establishing a safer and longer-lasting transportation network.

Keywords

• highway bridge engineering
• bridgeguard-vision
• bcnn-had
• advanced image processing
• sensor fusion
• smart transportation systems