Advancing autonomous navigation: YOLO-based road obstacle detection and segmentation for Bangladeshi environments
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
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| Langue: | English |
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Brac University
2024
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| Accès en ligne: | http://hdl.handle.net/10361/24349 |
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10361-243492024-10-20T21:02:54Z Advancing autonomous navigation: YOLO-based road obstacle detection and segmentation for Bangladeshi environments Mahmud, Ishtiaque Ritu, Sumaia Arefin Mahmood, Zaki Zawad Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University YOLOv8 YOLOv5 Automated vehicles YOLOv7 Obstacle detection Navigational intelligence Traffic safety--Bangladesh. Computer vision. Traffic engineering. Automotive engineering. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages 65-68). The advancement of autonomous vehicles requires a fast and effective object detection and segmentation system handling a wide range of road environments. The goal of this research is to improve autonomous navigation by applying fast and popular YOLO-based models for road obstacle detection and segmentation in South Asian countries, especially Bangladesh. Our team has compiled an extensive collection of videos taken on Bangladeshi streets using a smartphone camera that shows a variety of road conditions such as potholes, speed bumps, barricades, and normal roads taking into account rainy, sunny, day and night environments. By using Roboflow annotation and sampling tools, these videos were sampled into images and annotated with both bounding boxes and bounding masks. Using our custom annotated dataset, we trained and refined YOLO-based object detection and segmentation models such as YOLOv5, YOLOv7, and YOLOv8. The YOLOv5x model trained on our custom dataset shows better results with the highest mAP50 and mAP50-95 scores of 0.876 and 0.647 respectively. However, the YOLOv7x model trained on our custom dataset gives the lowest performance outcome with mAP50 and mAP50-95 scores of 0.583 and 0.331. Also, while comparing the models trained with custom dataset with models trained with a benchmark dataset, our dataset shows major improvements in results. We deployed the models on our local computer to allow real-time object detection with a camera, upon which our prototype car can take decisions using a microprocessor. This implementation reflects the feasibility of effective object detection and segmentation with limited resources. This research intends to optimize autonomous vehicle navigation in Bangladeshi road environments quickly and effectively. The outcomes of our experiments suggest that our approach offers a viable way to improve the security and efficiency of autonomous navigation in these kinds of settings. By addressing the unique challenges with road infrastructure in developing nations, our research advances the area of autonomous driving and creates opportunities for more customized and adaptive navigation systems. Ishtiaque Mahmud Sumaia Arefin Ritu Zaki Zawad Mahmood B.Sc. in Computer Science 2024-10-20T06:05:56Z 2024-10-20T06:05:56Z ©2024 2024-05 Thesis ID 20301311 ID 24141196 ID 23241139 http://hdl.handle.net/10361/24349 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 77 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
YOLOv8 YOLOv5 Automated vehicles YOLOv7 Obstacle detection Navigational intelligence Traffic safety--Bangladesh. Computer vision. Traffic engineering. Automotive engineering. |
| spellingShingle |
YOLOv8 YOLOv5 Automated vehicles YOLOv7 Obstacle detection Navigational intelligence Traffic safety--Bangladesh. Computer vision. Traffic engineering. Automotive engineering. Mahmud, Ishtiaque Ritu, Sumaia Arefin Mahmood, Zaki Zawad Advancing autonomous navigation: YOLO-based road obstacle detection and segmentation for Bangladeshi environments |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Mahmud, Ishtiaque Ritu, Sumaia Arefin Mahmood, Zaki Zawad |
| format |
Thesis |
| author |
Mahmud, Ishtiaque Ritu, Sumaia Arefin Mahmood, Zaki Zawad |
| author_sort |
Mahmud, Ishtiaque |
| title |
Advancing autonomous navigation: YOLO-based road obstacle detection and segmentation for Bangladeshi environments |
| title_short |
Advancing autonomous navigation: YOLO-based road obstacle detection and segmentation for Bangladeshi environments |
| title_full |
Advancing autonomous navigation: YOLO-based road obstacle detection and segmentation for Bangladeshi environments |
| title_fullStr |
Advancing autonomous navigation: YOLO-based road obstacle detection and segmentation for Bangladeshi environments |
| title_full_unstemmed |
Advancing autonomous navigation: YOLO-based road obstacle detection and segmentation for Bangladeshi environments |
| title_sort |
advancing autonomous navigation: yolo-based road obstacle detection and segmentation for bangladeshi environments |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/24349 |
| work_keys_str_mv |
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| _version_ |
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