SHM-Traffic: DRL and Transfer Learning Based UAV Control for Structural Health Monitoring of Bridges With Traffic

This work focuses on using advanced techniques for Structural Health Monitoring (SHM) for bridges with Traffic (SHM-Traffic). We propose an approach using Deep Reinforcement Learning (DRL)-based control for Unmanned Aerial Vehicle (UAV). Our approach conducts a concrete bridge deck survey while traffic is ongoing and detects cracks. The UAV performs the crack detection, and the location of cracks is initially unknown. We use two edge detection techniques. First, we use canny edge detection for crack detection. We also use a Convolutional Neural Network (CNN) for crack detection and compare it with canny edge detection. Transfer learning is applied using CNN with pre-trained weights obtained from a crack image dataset. This enables the model to adapt and improve its performance in identifying and localizing cracks. Proximal Policy Optimization (PPO) is applied for UAV control and bridge surveys. The experimentation across various scenarios is performed to evaluate the performance of the proposed methodology. Key metrics such as task completion time and reward convergence are observed to gauge the effectiveness of the approach. We observe that the Canny edge detector offers up to 40% lower task completion time, while the CNN excels in up to 12% better damage detection and 1.8 times better rewards.