Object tracking for autonomous vehicle using YOLO V3
Accuracy and performance of an object detection model have always been the main requirements for an object tracking system. In this project, the performance of machine learning based object detection using YOLO v3 technique will be investigated. Two models were provided where one model is trained us...
| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
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Springer Science and Business Media Deutschland GmbH
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/39447/ http://umpir.ump.edu.my/id/eprint/39447/1/Object%20Tracking%20for%20Autonomous%20Vehicle%20Using%20YOLO%20V3.pdf http://umpir.ump.edu.my/id/eprint/39447/2/Object%20tracking%20for%20autonomous%20vehicle%20using%20YOLO%20V3_ABS.pdf |
| _version_ | 1848825771825561600 |
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| author | Hung, William Chin Wei Muhammad Aizzat, Zakaria Muhammad Izhar, Ishak Mohamad Heerwan, Peeie |
| author_facet | Hung, William Chin Wei Muhammad Aizzat, Zakaria Muhammad Izhar, Ishak Mohamad Heerwan, Peeie |
| author_sort | Hung, William Chin Wei |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Accuracy and performance of an object detection model have always been the main requirements for an object tracking system. In this project, the performance of machine learning based object detection using YOLO v3 technique will be investigated. Two models were provided where one model is trained using online Common Objects in Contact (COCO) dataset only, and the other model is trained with additional images from Universiti Malaysia Pahang (UMP) with several different locations dataset. The performance of the trained models were evaluated using mean Average Precision (mAP), and precision techniques. The model with highest precision was selected to be implemented on actual road test. The results show that the model 2 has the highest precision and was able to detect every class of objects. Each output box had displayed the class and the distance to the objects from the RGBD camera of the vehicle. It is observed that the first model that was trained to perform the mAP value of 90.2% and a performance of 0.484 precision. For the second model, it can be seen that the accuracy of the detections are higher than that of model 1. Therefore, model 2 has a better performance with a value of 0.596 precision. |
| first_indexed | 2025-11-15T03:34:14Z |
| format | Conference or Workshop Item |
| id | ump-39447 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:34:14Z |
| publishDate | 2022 |
| publisher | Springer Science and Business Media Deutschland GmbH |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-394472023-11-30T07:11:47Z http://umpir.ump.edu.my/id/eprint/39447/ Object tracking for autonomous vehicle using YOLO V3 Hung, William Chin Wei Muhammad Aizzat, Zakaria Muhammad Izhar, Ishak Mohamad Heerwan, Peeie T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Accuracy and performance of an object detection model have always been the main requirements for an object tracking system. In this project, the performance of machine learning based object detection using YOLO v3 technique will be investigated. Two models were provided where one model is trained using online Common Objects in Contact (COCO) dataset only, and the other model is trained with additional images from Universiti Malaysia Pahang (UMP) with several different locations dataset. The performance of the trained models were evaluated using mean Average Precision (mAP), and precision techniques. The model with highest precision was selected to be implemented on actual road test. The results show that the model 2 has the highest precision and was able to detect every class of objects. Each output box had displayed the class and the distance to the objects from the RGBD camera of the vehicle. It is observed that the first model that was trained to perform the mAP value of 90.2% and a performance of 0.484 precision. For the second model, it can be seen that the accuracy of the detections are higher than that of model 1. Therefore, model 2 has a better performance with a value of 0.596 precision. Springer Science and Business Media Deutschland GmbH 2022 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39447/1/Object%20Tracking%20for%20Autonomous%20Vehicle%20Using%20YOLO%20V3.pdf pdf en http://umpir.ump.edu.my/id/eprint/39447/2/Object%20tracking%20for%20autonomous%20vehicle%20using%20YOLO%20V3_ABS.pdf Hung, William Chin Wei and Muhammad Aizzat, Zakaria and Muhammad Izhar, Ishak and Mohamad Heerwan, Peeie (2022) Object tracking for autonomous vehicle using YOLO V3. In: Lecture Notes in Electrical Engineering; Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2021 , 20 September 2021 , Gambang, Kuantan. pp. 265-273., 900 (277979). ISSN 1876-1100 ISBN 978-981192094-3 (Published) |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures Hung, William Chin Wei Muhammad Aizzat, Zakaria Muhammad Izhar, Ishak Mohamad Heerwan, Peeie Object tracking for autonomous vehicle using YOLO V3 |
| title | Object tracking for autonomous vehicle using YOLO V3 |
| title_full | Object tracking for autonomous vehicle using YOLO V3 |
| title_fullStr | Object tracking for autonomous vehicle using YOLO V3 |
| title_full_unstemmed | Object tracking for autonomous vehicle using YOLO V3 |
| title_short | Object tracking for autonomous vehicle using YOLO V3 |
| title_sort | object tracking for autonomous vehicle using yolo v3 |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TK Electrical engineering. Electronics Nuclear engineering TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/39447/ http://umpir.ump.edu.my/id/eprint/39447/1/Object%20Tracking%20for%20Autonomous%20Vehicle%20Using%20YOLO%20V3.pdf http://umpir.ump.edu.my/id/eprint/39447/2/Object%20tracking%20for%20autonomous%20vehicle%20using%20YOLO%20V3_ABS.pdf |