YOLO-based network fusion for riverine floating debris monitoring system
Document Type
Conference Item
Publication Date
1-1-2021
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.
Keywords
Augmentation techniques, Classification accuracy, Debris monitoring, Management systems, Monitoring system, Precision and recall, Robotic platforms, Visual information
Divisions
sch_ecs
Funders
Kurita Water and Environment Foundation [Grant No: KWEF]
Publication Title
3rd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2021
Event Title
3rd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2021
Event Location
Kuala Lumpur
Event Dates
12-13 June 2021
Event Type
conference