International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Deep Learning-Based Damaged Road Detection |
|---|---|
| ABSTRACT | Road damage detection is an important task for improving road safety, reducing maintenance costs, and preventing accidents. Traditional road inspection methods are time-consuming, labor-intensive, and often inaccurate. This project proposes an automated road damage detection system using deep learning and computer vision techniques. The system analyzes road images captured by cameras or mobile devices to identify different types of road damage, such as potholes, cracks, and surface deterioration. A convolutional neural network (CNN) or an attention-based object detection model is trained on labeled road images to accurately detect and classify damaged areas. The proposed method provides faster and more reliable road inspection compared to manual methods |
| AUTHOR | DR.T.V. S SRIRAM, D. APPALA RAJU, K.DHANA LAXMI Sr. Asst. Professor, Dept. of MCA, NSRIT, AP, India Asst. Professor, Dept. of MCA, NSRIT, AP, India Dept. of MCA, NSRIT, Visakhapatnam, AP, India |
| VOLUME | 185 |
| DOI | DOI: 10.15680/IJIRCCE.2026.14060116 |
| pdf/116_Deep Learning-Based Damaged Road Detection.pdf | |
| KEYWORDS | |
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