IJCOPE Journal

UGC Logo DOI / ISO Logo

International Journal of Creative and Open Research in Engineering and Management

A Peer-Reviewed, Open-Access International Journal Supporting Multidisciplinary Research, Digital Publishing Standards, DOI Registration, and Academic Indexing.
Journal Information
ISSN: 3108-1754 (Online)
Crossref DOI: Available
ISO Certification: 9001:2015
Publication Fee: 599/- INR
Compliance: UGC Journal Norms
License: CC BY 4.0
Peer Review: Double Blind
Volume 02, Issue 8

Published on: August 2026

MULTI-MODAL EDGE-AI SYSTEM FOR REAL-TIME ROAD HEALTH DIAGNOSTICS USING VISION-ACOUSTIC SENSOR FUSION

Rishika S Niharika K Thanuja K B Rishi H

Mrs. Supriya V

Department of Electronics and Communication Engineering, K S School of Engineering and Management, Bengaluru, India
DOI:https://doi.org/10.55041/ijcope.v2i8.068

Article Status

Plagiarism Passed Peer Reviewed Open Access

Available Documents

Download PDF Review Report

Abstract

Road surface defects such as potholes are a major concern for road safety, vehicle performance, and infrastructure maintenance. Traditional inspection methods are often time-consuming, expensive, and unsuitable for continuous monitoring. This paper presents a Multi-Modal Edge AI System for Real-Time Road Health Diagnostics Using Vision-Acoustic Sensor Fusion. The proposed system combines OpenCV-based computer vision with vibration sensor data to improve the accuracy and reliability of pothole detection. A Raspberry Pi performs real-time edge processing, while GPS records the location of detected road defects for visualization on Google Maps. By integrating multiple sensing techniques, the system minimizes false detections and enables efficient road condition monitoring without relying on continuous cloud connectivity. The proposed approach provides a low-cost, scalable, and practical solution for intelligent transportation systems, smart city applications, and timely road maintenance.

How to Cite this Paper

S, R., K, N., B, T. K. & H, R. (2026). Multi-Modal Edge-AI System for Real-Time Road Health Diagnostics using Vision-Acoustic Sensor Fusion. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(8), 1-9. https://doi.org/10.55041/ijcope.v2i8.068

S, Rishika, et al.. "Multi-Modal Edge-AI System for Real-Time Road Health Diagnostics using Vision-Acoustic Sensor Fusion." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 8, 2026, pp. 1-9. doi:https://doi.org/10.55041/ijcope.v2i8.068.

S, Rishika,Niharika K,Thanuja B, and Rishi H. "Multi-Modal Edge-AI System for Real-Time Road Health Diagnostics using Vision-Acoustic Sensor Fusion." International Journal of Creative and Open Research in Engineering and Management 02, no. 8 (2026): 1-9. https://doi.org/https://doi.org/10.55041/ijcope.v2i8.068.

Search & Index

Google Scholar Crossref DOI ResearchGate

References


  • Wu, K. Chen, and T. Liang, "Edge-Cloud Collaborative Pothole Detection via Federated Temporal Segmentation," 2026.

  • Romero, H. Mantz, T. Walter, and M. Schneider, "Multi-Sensor Pothole Detection and Monitoring System," 2025.

  • G. Nair, J. Baskar, V. Kumar, and A. Krishnan, "DeepRoadGuard: Enhanced Pothole Detection Using Bayesian Fusion and Night-Vision Data," 2025.

  • H. Asad, S. Khaliq, M. H. Yousaf, M. O. Ullah, A. Ahmad, and Q. Chen, "Pothole Detection Using Deep Learning: A Real-Time and AI-on-the-Edge Perspective," Advances in Civil Engineering, vol. 2022, pp. 1–16, 2022.

  • Zhong, D. Kong, Y. Wei, B. Pan, et al., "YOLOv8 and Point Cloud Fusion for Enhanced Road Pothole Detection and Quantification," 2025.

  • Damayanti and Z. P. Putra, "Deploying Edge AI for Road Surface Damage Detection Using TensorFlow Lite and Teachable Machine," 2025.

  • Sharma, P. Verma, R. Gupta, and A. Kumar, "Edge Intelligence-Based Road Damage Detection Using Lightweight Deep Learning Models," 2024.

  • Ghafoor, M. A. M. Sadeeq, and S. R. M. Zeebaree, "IoT and Cloud Based Automated Pothole Detection Model Using Extreme Gradient Boosting," 2023.


 

  • Ghogho et al., "Sustainable Road Pothole Detection: A Crowdsourcing Based Multi-Sensors Fusion Approach," 2023.

  • -T. Huang, M. R. Jahanshahi, F. Shen, and



  1. G. Mondal, "Deep Learning–Based Autonomous Road Condition Assessment Leveraging Inexpensive RGB and Depth Sensors and Heterogeneous Data Fusion: Pothole Detection and Quantification," Journal of Transportation Engineering, Part B: Pavements, vol. 149, no. 2, 2023.

Ethical Compliance & Review Process

  • •All submissions are screened under plagiarism detection.
  • •Review follows editorial policy.
  • •Authors retain copyright.
  • •Peer Review Type: Double-Blind Peer Review
  • •Published on: Aug 08 2026
CCBYNC

This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.

View License
Back to Volume 02, Issue 8View All Issues Next Article

← Previous Article

Modification of Drill Honing Tool for Engine Head Burr Cleaning

Next Article →

Narrating the Psychological Experiences of Reminiscence, Matrimonial Relationships, Identity and Human Search for Belonging in Avni Doshi’s ‘The First House’
Scroll to Top