Deep learning-driven UAV vision for automated road crack detection and classification

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ABSTRACT Unmanned aerial vehicles are increasingly utilised for monitoring and inspecting critical infrastructure such as power generation grids, oil and gas pipelines and roads. One key task in road maintenance is the detection of cracks, which is crucial for ensuring road safety. Manual detection of cracks is time-consuming and prone to errors, highlighting the need for automated solutions. This study presents an automated method for road crack detection and classification using a hybrid deep learning technique. The proposed approach integrates a pyramid vision transformer and ConvMixer models for feature extraction, enabling the system to learn complex patterns in crack images. Image pre-processing is first performed using a median filtering technique to enhance image quality. The detection and classification of cracks are then carried out using an Elman neural network model, with its hyperparameters optimised through an improved black widow optimisation algorithm. Extensive simulations demonstrate that the proposed method outperforms other deep learning models in terms of performance, providing a reliable and efficient solution for automated crack detection.