Leveraging the power of object detection models in identifying litter for a significant reduction in environmental pollution

Lim Zhen Xian and Ervin Gubin Moung and Jason Teo Tze Wi and Nordin Saad and Farashazillah Yahya and Tiong Lin Rui and Ali Farzamnia (2023) Leveraging the power of object detection models in identifying litter for a significant reduction in environmental pollution. In: 2023 13th International Conference on Computer and Knowledge Engineering (ICCKE), 01-02 NOVEMBER 2023, Mashhad, Iran, Islamic Republic.

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Official URL: https://ieeexplore.ieee.org/document/10326292

Abstract

The growing concern of litter pollution in natural environments has escalated into a significant issue that demands immediate and efficient resolution. Recent studies have used deep learning models to solve the problem of litter pollution, but these approaches have faced challenges in accurately detecting litter in real-world environments. Therefore, this paper has proposed a litter detection model and analyze its performance on the TACO dataset, which contains real-world outdoor environment images. The paper evaluates three distinct deep learning models (YOLOv4, YOLOv5, Faster R-CNN) and identifies the best performing model. The performance of the selected model is then enhanced through adjustments of hyperparameters, use of several preprocessing techniques and data augmentation techniques. The experimental results showed that YOLOv5x achieved 88% mAP@.5 and 71.4% mAP@.75 on testing dataset which outperformed the state-of-art studies. The findings of this paper provide valuable insights into the solution of litter pollution and can inform future research in this area.

Item Type:	Conference or Workshop Item (Paper)
Keyword:	Litter detection, Object Detection, YOLOv5, TACO dataset, Optimal setup
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1-2040 Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics T Technology > TD Environmental technology. Sanitary engineering > TD1-1066 Environmental technology. Sanitary engineering > TD172-193.5 Environmental pollution
Department:	FACULTY > Faculty of Computing and Informatics
Depositing User:	JUNAINE JASNI -
Date Deposited:	11 Aug 2025 11:14
Last Modified:	13 Aug 2025 11:32
URI:	https://eprints.ums.edu.my/id/eprint/44812

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