Deep learning-driven UAV vision for automated road crack detection and classification.
Saved in:
| Title: | Deep learning-driven UAV vision for automated road crack detection and classification. |
|---|---|
| Authors: | Rathod, Vaishnavee V.1 (AUTHOR) d20co004@coed.svnit.ac.in, Rana, Dipti P.1 (AUTHOR), Mehta, Rupa G.1 (AUTHOR) |
| Source: | Nondestructive Testing & Evaluation. Jul2026, Vol. 41 Issue 7, p4298-4327. 30p. |
| Subjects: | Deep learning, Automatic classification, Optimization algorithms, Drone photography, Engineering inspection, Recurrent neural networks, Transformer models |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Nondestructive Testing & Evaluation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
Be the first to leave a comment!
