Optimizing Waste Classification Model using YOLOv11Architecture

Optimizing Waste Classification Model using YOLOv11Architecture

Waste,Classification, YOLOv11, CNN

Municipal solid waste management remains a critical challenge due to rapid urbanizationand consumption patterns. This study proposed a image basedwaste classification modelfor organic, inorganic, and hazardous (B3) waste using the YOLOv11 architecture.To conduct the study, we gathereda hugedataset of 5,000 images across daylight, dusk, and night conditions. According to experimental results, the proposed model can achieve an [email protected] of 70%, a precision of 69%, a recall of 70%, and an F1-score of 0.70, operating at 43 frames per second (FPS) with 102 GFLOPs.It canconfirmits suitability for real-time applications in resource-constrained environments. Compared to heavier deep learning models, this efficiency-performance balance highlights the practical advantage of YOLOv11 for continuous waste monitoring and automated sorting systems.

Bradika Almandin Wisesa1, Vivin Mahat Putri2, Evvin Faristasari3, Sirlus Andreanto Jasman Duli4, Rahmat Lionza

https://ijicom.respati.ac.id/index.php/ijicom/article/view/212/131

nternational Journal of Informatics and Computation (IJICOM)

2025

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