Leveraging the Power of Object Detection Models in Identifying Litter for a Significant Reduction in Environmental Pollution

Lim Zhen Xian, Ervin Gubin Moung, Jason Teo Tze Wi, Nordin Saad, Farashazillah Yahya, Tiong Lin Rui, Ali Farzamnia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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% [email protected] and 71.4% [email protected] 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.

Original languageEnglish
Title of host publication2023 13th International Conference on Computer and Knowledge Engineering, ICCKE 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350330151
ISBN (Print)9798350330168
Publication statusPublished - 27 Nov 2023
Externally publishedYes
Event13th International Conference on Computer and Knowledge Engineering - Mashhad, Iran, Islamic Republic of
Duration: 1 Nov 20232 Nov 2023
Conference number: 13

Publication series

NameInternational Conference on Computer and Knowledge Engineering
ISSN (Print)2375-1304
ISSN (Electronic)2643-279X


Conference13th International Conference on Computer and Knowledge Engineering
Abbreviated titleICCKE 2023
Country/TerritoryIran, Islamic Republic of
Internet address

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