Zailan, Nur Athirah and Mohd Khairuddin, Anis Salwa and Khairuddin, Uswah and Taguchi, Akira (2021) YOLO-based network fusion for riverine floating debris monitoring system. In: 3rd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2021, 12-13 June 2021, Kuala Lumpur.
Full text not available from this repository.Abstract
Riverine floating debris has been one of the major challenges and a well-known issue across the globe for decades. To mitigate this problem, sources of debris and their pathways to the riverine environment need to be identified and quantified. The scope of this study is to obtain visual information of floating debris which is crucial in developing a robotic platform for riverine management system. Therefore, an object detector using You Only Look Once version 4 (YOLOv4) algorithm is developed to detect floating debris for the riverine monitoring system. The debris detection system is trained on five object classes such as styrofoam, plastic bags, plastic bottle, aluminium can and plastic container. After the first training is conducted, image augmentation technique is implemented to increase training and validation datasets. Finally, the performance of the proposed debris detection system is evaluated based on the highest mean average precision (mAP) weight file, classification accuracy, precision and recall. © 2021 IEEE.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Funders: | Kurita Water and Environment Foundation [Grant No: KWEF] |
| Uncontrolled Keywords: | Augmentation techniques; Classification accuracy; Debris monitoring; Management systems; Monitoring system; Precision and recall; Robotic platforms; Visual information |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 10 Jul 2024 07:33 |
| Last Modified: | 10 Jul 2024 07:33 |
| URI: | http://eprints.um.edu.my/id/eprint/36025 |
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