International Journal of
Advanced Multidisciplinary Application

Volume 3, Issue 5

• Author(s)Vikram S. Bhattacharya, Ananya R. Krishnan
• AffiliationDepartment of Civil Engineering, Savith University, Chennai
• Page No.1-4
• Volume, Issue & YearVolume 3, Issue 5, May 2026
• Published On2026/05/01
• JournalInternational Journal of Advanced Multidisciplinary Application (IJAMA)
• ISSN No.3048-9350
• DOIhttps://doi.org/10.5281/zenodo.20006659

Abstract

Routine visual inspection of concrete bridges is labour-intensive, subjective and increasingly impractical given India's expanding network of over 175 000 road bridges. This paper presents an edge-AI inspection platform that performs real-time crack detection, segmentation and severity classification onboard an unmanned aerial vehicle. A lightweight encoder–decoder network combining a MobileNet-V3 backbone with a U-Net decoder was trained on a curated dataset of 11 240 bridge surface images and deployed on an NVIDIA Jetson Orin Nano edge device. The proposed model achieved a mean intersection-over-union of 0.86 and a sustained inference rate of 22 frames per second at 512×512 input resolution, outperforming three baseline architectures while remaining within a 7 W power envelope. Field validation across two bridge structures in Karnataka demonstrated reliable detection of hairline through severe crack categories with geo-tagged outputs streamed to a cloud dashboard for engineering review

Keywords: Structural health monitoring; Crack segmentation; Edge AI; Convolutional neural networks; UAV inspection; Concrete bridges.

References

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