Innovative Crack Detection Approach Using UAV-Based Imaging Technology

The Aging Road bridges impart a serious threat that must be addressed by estimating the health of the bridges manually. This paper presents a novel approach for crack detection in road bridge by using Unmanned Aerial Vehicles (UAVs). Traditional bridge inspection methods prove to be time consuming, expensive and have inaccessible areas. UAVs equipped with CNN trained model, high-resolution cameras, and sensors offer safe and secure ways to inspect the bridge health. This paper explores the integration of deep learning models with the UAVs in detecting cracks of a bridge which offers valuable insights on the health of a road bridge. The CNN model is trained with a dataset of 5000 images for each label, namely Positive and negative. The results establish an accuracy of more than 97 percent. This paper further emphasizes the potential of UAVs and crack detection for infrastructure maintenance, offering real-time data to support repairs and improve long term bridge health.