Automatic Detection of Damaged Roads and Lane Detection using Deep Learning
This project introduces an automated system for detecting road surface damages and identifying lane markings using Deep Learning, YOLO (You Only Look Once), and Canny edge detection. The main goal is to improve road safety, assist autonomous navigation, and support efficient infrastructure maintenan...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
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INTI International University
2025
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2153/ http://eprints.intimal.edu.my/2153/1/jods2025_11.pdf http://eprints.intimal.edu.my/2153/2/696 |
| _version_ | 1848766934933307392 |
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| author | Kotakonda Sandhya, Rani Kommi, Chandana Nallamalla, Kavya Kasireddy, Poojitha Thalla, Pallavi |
| author_facet | Kotakonda Sandhya, Rani Kommi, Chandana Nallamalla, Kavya Kasireddy, Poojitha Thalla, Pallavi |
| author_sort | Kotakonda Sandhya, Rani |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | This project introduces an automated system for detecting road surface damages and identifying lane markings using Deep Learning, YOLO (You Only Look Once), and Canny edge detection. The main goal is to improve road safety, assist autonomous navigation, and support efficient infrastructure maintenance. Road damages, such as potholes and cracks, are detected in real-time from images or videos captured by cameras mounted on vehicles or drones. The YOLO algorithm is used to classify and localize these damages with high speed and accuracy. At the same time, the Canny edge detection method identifies lane boundaries, ensuring precise lane detection even in challenging environments. Combining these techniques results in a reliable and scalable solution for smart transportation systems. The system reduces the need for manual road inspection and enables authorities to prioritize repairs based on real-time information. It also supports safer navigation for autonomous and assisted vehicles. |
| first_indexed | 2025-11-14T11:59:02Z |
| format | Article |
| id | intimal-2153 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:59:02Z |
| publishDate | 2025 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-21532025-07-04T02:51:59Z http://eprints.intimal.edu.my/2153/ Automatic Detection of Damaged Roads and Lane Detection using Deep Learning Kotakonda Sandhya, Rani Kommi, Chandana Nallamalla, Kavya Kasireddy, Poojitha Thalla, Pallavi QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering This project introduces an automated system for detecting road surface damages and identifying lane markings using Deep Learning, YOLO (You Only Look Once), and Canny edge detection. The main goal is to improve road safety, assist autonomous navigation, and support efficient infrastructure maintenance. Road damages, such as potholes and cracks, are detected in real-time from images or videos captured by cameras mounted on vehicles or drones. The YOLO algorithm is used to classify and localize these damages with high speed and accuracy. At the same time, the Canny edge detection method identifies lane boundaries, ensuring precise lane detection even in challenging environments. Combining these techniques results in a reliable and scalable solution for smart transportation systems. The system reduces the need for manual road inspection and enables authorities to prioritize repairs based on real-time information. It also supports safer navigation for autonomous and assisted vehicles. INTI International University 2025-06 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2153/1/jods2025_11.pdf text en cc_by_4 http://eprints.intimal.edu.my/2153/2/696 Kotakonda Sandhya, Rani and Kommi, Chandana and Nallamalla, Kavya and Kasireddy, Poojitha and Thalla, Pallavi (2025) Automatic Detection of Damaged Roads and Lane Detection using Deep Learning. Journal of Data Science, 2025 (11). pp. 1-14. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Kotakonda Sandhya, Rani Kommi, Chandana Nallamalla, Kavya Kasireddy, Poojitha Thalla, Pallavi Automatic Detection of Damaged Roads and Lane Detection using Deep Learning |
| title | Automatic Detection of Damaged Roads and Lane Detection using Deep Learning |
| title_full | Automatic Detection of Damaged Roads and Lane Detection using Deep Learning |
| title_fullStr | Automatic Detection of Damaged Roads and Lane Detection using Deep Learning |
| title_full_unstemmed | Automatic Detection of Damaged Roads and Lane Detection using Deep Learning |
| title_short | Automatic Detection of Damaged Roads and Lane Detection using Deep Learning |
| title_sort | automatic detection of damaged roads and lane detection using deep learning |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.intimal.edu.my/2153/ http://eprints.intimal.edu.my/2153/ http://eprints.intimal.edu.my/2153/1/jods2025_11.pdf http://eprints.intimal.edu.my/2153/2/696 |