International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Pothole finding and reporting system

Author(s) Mr. MOHIT RAJ, Mr. DEEPESH DEWANGAN
Country India
Abstract Accurate pothole detection in real-world environments is hindered by noise interference, limited severity assessment, and poor recognition of small and distant potholes. To address these challenges, this paper proposes a novel pothole finding and reporting system based on YOLOv10 integrated with the Lifting Wavelet Transform (LWT). YOLOv10 enables efficient multi-scale spatial feature learning for real-time detection, while LWT extracts robust multi-resolution texture in-formation that enhances sensitivity to subtle road surface irregularities. The fusion of spatial features and wavelet coefficients improves noise resilience, strengthens small and distant pothole detection, and facili-tates indirect severity characterization. Experimental evaluation con-ducted on publicly available road damage datasets demonstrates that the proposed approach achieves an accuracy of 96.8% and a mean Av-erage Precision (mAP) of 94.2%, outperforming conventional YOLO-based methods. Despite incorporating wavelet-based enhancement, the model remains computationally efficient with only 28.6 million param-eters, making it suitable for real-time deployment on edge and mobile devices. The proposed system offers a scalable and intelligent solution for smart road infrastructure monitoring.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 3, May-June 2026
Published On 2026-05-09
DOI https://doi.org/10.36948/ijfmr.2026.v08i03.77780
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E-ISSN 2582-2160
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