| dc.description.abstract |
Integrating Unmanned Aerial Vehicle (UAV) technology with Artificial Intelligence AI and Computer Vision has revolutionized asset management, particularly pavement health monitoring. However, current AI-based methods often struggle in low-visibility scenarios, limiting their effectiveness. To address this, we present a novel end-to-end deep learning pipeline that detects image degradation using an efficient Attention mechanism and performs subsequent enhancement. This algorithm can be seamlessly integrated into drones or used for post-processing of pavement imagery. Its efficiency allows for scalability, making it a valuable tool for downstream road health monitoring tasks, such as cost estimation for road repairs. Our approach achieves mean accuracies of 93.34% with a mean inference time of 0.154 sec., demonstrating its efficacy. |
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