| Title: | OS13-3 Exploring the Performance of YOLOv11: Detecting Compostable and Non-Compostable Kitchen Waste in Real-Time Applications |
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| Publication: | ICAROB2025 |
| Volume: | 30 |
| Pages: | 392-397 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2025.OS13-3 |
| Author(s): | Ain Atiqa Mustapha, Sarah Atifah Saruchi, Mahmud Iwan Solihin, Fatima Karam Aldeen, Ammar A Al-Talib |
| Publication Date: | February 13, 2025 |
| Keywords: | YOLOv11, Object Detection, Transformer-based attention, Mean Average Precision (mAP) |
| Abstract: | This paper investigates the advancements of YOLOv11, the latest model in the YOLO series in real-time object detection tasks on small datasets of compostable and non-compostable kitchen waste. Using a custom compostable and non-compostable kitchen waste dataset, YOLOv11 achieves an accuracy of 90.7% and a mean Average Precision (mAP) of 0.91, with a reduced inference time of 10.5 milliseconds. The study highlights YOLOv11's architectural enhancements, training methodology, and potential applications in waste management. While YOLOv11 sets a new benchmark in object detection, challenges like high computational demands, paving the way for future research on optimization for edge devices |
| PDF File: | https://alife-robotics.co.jp/members2025/icarob/data/html/data/OS/OS13/OS13-3.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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