Identification and comparative analysis of potholes using image processing techniques
| bracu.type.group | Research Publications | |
| datacite.rights | Metadata Only | |
| dc.contributor.author | Ahmed, Asif | |
| dc.contributor.author | Islam, Samiul | |
| dc.contributor.author | Chakrabarty, Amitabha | |
| dc.contributor.department | Department of Electrical and Electronic Engineering | |
| dc.date.accessioned | 2026-09-01T07:53:14Z | |
| dc.date.available | 2026-09-01T07:53:14Z | |
| dc.date.issued | 2019-06-01 | |
| dc.description.abstract | Potholes have become major havoc and are the leading reason for the damage of road transport vehicles. Hence, it is important to asses this problem and to provide a solution which can aid the driver of the vehicle before approaching a pothole. The topic selected uses 4 different image segmentation techniques to identify all types of potholes. The following techniques that were worked on were Image Thresholding, Canny Edge Detection, K-Means clustering, and Fuzzy C-Means clustering. Performance analysis of the different image segmentation techniques is done in MATLAB 2015Ra image processing toolbox. The effectiveness of the different image segmentation techniques was then tested in different environments. Thus the results were generated in terms of accuracy and precision. Moreover, the results were compared with each other to draw a conclusion on their viability. Finally, the paper emphasizes why these techniques are good for developed infrastructure and why they are not for third world countries. | |
| dc.description.version | Published | |
| dc.format.extent | 497-502 | |
| dc.identifier.citation | A. Ahmed, S. Islam and A. Chakrabarty, "Identification and Comparative Analysis of Potholes using Image Processing Techniques," 2019 IEEE Region 10 Symposium (TENSYMP), Kolkata, India, 2019, pp. 497-502, doi: 10.1109/TENSYMP46218.2019.8971385. | |
| dc.identifier.doi | 10.1109/TENSYMP46218.2019.8971385 | |
| dc.identifier.issn | 9781728102979 | |
| dc.identifier.other | 2-s2.0-85079293256 | |
| dc.identifier.uri | https://hdl.handle.net/10361/29655 | |
| dc.language.iso | en_US | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.hasversion | 10.1109/TENSYMP46218.2019.8971385 | |
| dc.relation.ispartof | Proceedings of 2019 IEEE Region 10 Symposium Tensymp 2019 | |
| dc.relation.ispartofseries | Proceedings of 2019 IEEE Region 10 Symposium Tensymp 2019 | |
| dc.relation.uri | https://ieeexplore.ieee.org/document/8971385 | |
| dc.rights | false | |
| dc.subject | Canny edge detection | |
| dc.subject | Fuzzy C-means clustering | |
| dc.subject | Image segmentation | |
| dc.subject | Image thresholding | |
| dc.subject | K-means clustering | |
| dc.subject | Pothole | |
| dc.subject | Pothole detection | |
| dc.subject.lcsh | Potholes. | |
| dc.subject.lcsh | Image processing. | |
| dc.title | Identification and comparative analysis of potholes using image processing techniques | |
| dc.type | Conference Proceeding | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.identifier.scopus-author-id | 57215116438 | |
| person.identifier.scopus-author-id | 57642181500 | |
| person.identifier.scopus-author-id | 35108854200 |