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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Recognition And Identification of Road Pavement Cracks Through Images By using Machine Learning Algorithm |
| Authors Name: | Rajesh R. Petkar , Jayant A.Patil |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_205586
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| Published Paper Id: | IJRTI2508001 |
| Published In: | Volume 10 Issue 8, August-2025 |
| DOI: | |
| Abstract: | The rapid growth of urban infrastructure has made the maintenance of road surfaces a critical issue for city planners and civil engineers. Pavement cracks and potholes significantly contribute to traffic accidents and long-term infrastructure degradation. Traditional inspection methods are labor-intensive, time-consuming, and subject to human error. With the emergence of computer vision and deep learning, particularly convolutional neural networks (CNNs), automated road surface analysis has become a practical solution. This project proposes a robust framework for detecting and classifying road surface defects—specifically cracks and potholes—using machine learning algorithms trained on annotated image datasets. High-resolution images of various road conditions are processed and fed into a CNN model, which learns visual features to differentiate between defect types and severities. The system integrates pre-processing steps like image enhancement, edge detection, and data augmentation to improve detection accuracy under varied lighting and environmental conditions. The trained model achieves high precision in identifying surface anomalies, outperforming conventional techniques. Evaluation metrics such as accuracy, recall, and F1-score are used to validate performance. The proposed method offers scalable deployment options in real-time road surveillance systems through drones or vehicle-mounted cameras. Furthermore, the model supports predictive maintenance planning by pinpointing early-stage defects. This initiative reduces human effort, increases monitoring efficiency, and ultimately enhances road safety. The system’s adaptability across diverse geographical terrains further highlights its practicality. With the integration of GPS and cloud storage, defect locations can be mapped and archived for future assessments. This AI-driven approach has the potential to revolutionize road maintenance and traffic safety management globally. |
| Keywords: | Pavement Crack Detection, Pothole Identification, Deep Learning, Convolutional Neural Network (CNN),Image Classification, Computer Vision, Road Maintenance, Surface Defect Detection, Edge Detection,Automated Inspection, Predictive Maintenance, Real-Time Detection, Accuracy Evaluation, Road Surface Analysis, Civil Engineering AI, Machine Learning Algorithms |
| Cite Article: | "Recognition And Identification of Road Pavement Cracks Through Images By using Machine Learning Algorithm ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 8, page no.a1-a6, August-2025, Available :http://www.ijrti.org/papers/IJRTI2508001.pdf |
| Downloads: | 000791 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2508001
Registration ID:205586
Published In: Volume 10 Issue 8, August-2025
DOI (Digital Object Identifier):
Page No: a1-a6 Country: Sangli, Maharshtra, India Research Area: Civil Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2508001 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2508001 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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