An automated solid waste detection using the optimized YOLO model for riverine management

Zailan, Nur Athirah and Azizan, Muhammad Mokhzaini and Hasikin, Khairunnisa and Khairuddin, Anis Salwa Mohd and Khairuddin, Uswah (2022) An automated solid waste detection using the optimized YOLO model for riverine management. Frontiers in Public Health, 10. ISSN 2296-2565, DOI

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Due to urbanization, solid waste pollution is an increasing concern for rivers, possibly threatening human health, ecological integrity, and ecosystem services. Riverine management in urban landscapes requires best management practices since the river is a vital component in urban ecological civilization, and it is very imperative to synchronize the connection between urban development and river protection. Thus, the implementation of proper and innovative measures is vital to control garbage pollution in the rivers. A robot that cleans the waste autonomously can be a good solution to manage river pollution efficiently. Identifying and obtaining precise positions of garbage are the most crucial parts of the visual system for a cleaning robot. Computer vision has paved a way for computers to understand and interpret the surrounding objects. The development of an accurate computer vision system is a vital step toward a robotic platform since this is the front-end observation system before consequent manipulation and grasping systems. The scope of this work is to acquire visual information about floating garbage on the river, which is vital in building a robotic platform for river cleaning robots. In this paper, an automated detection system based on the improved You Only Look Once (YOLO) model is developed to detect floating garbage under various conditions, such as fluctuating illumination, complex background, and occlusion. The proposed object detection model has been shown to promote rapid convergence which improves the training time duration. In addition, the proposed object detection model has been shown to improve detection accuracy by strengthening the non-linear feature extraction process. The results showed that the proposed model achieved a mean average precision (mAP) value of 89%. Hence, the proposed model is considered feasible for identifying five classes of garbage, such as plastic bottles, aluminum cans, plastic bags, styrofoam, and plastic containers.

Item Type: Article
Funders: Industry-Driven Innovation Grant (IDIG) by Universiti Malaya, [PPSI-2020-CLUSTER-SD01]
Uncontrolled Keywords: Computer vision; Image processing; Object detection; Smart city; Urbanization; Water quality
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
T Technology > TD Environmental technology. Sanitary engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 20 Sep 2023 02:04
Last Modified: 20 Sep 2023 02:04

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