| Title: | OS23-5 Object Detection and Instance Segmentation with YOLOV8: Progress and Limitations |
|---|---|
| Publication: | ICAROB2024 |
| Volume: | 29 |
| Pages: | 724-728 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2024.OS23-5 |
| Author(s): | L. J. Lee, Hazry Desa, Muhammad Azizi Azizan, A. -S. T. Hussain, M. H. Tanveer |
| Publication Date: | February 22, 2024 |
| Keywords: | Image Processing YoloV8, COCO, Segmentation, Object detection |
| Abstract: | This research employs object detection and instance segmentation algorithms to distinguish between objects and backgrounds and to interpret the detected objects. The YOLOV8 (You Only Look Once) framework and COCO dataset are utilized for detecting and interpreting the objects. Additionally, the accuracy of detection, segmentation, and interpretation is tested by placing objects at various distances from the camera. The algorithm's performance was evaluated, and the results were documented. In the experiments, a sample of 11 objects was tested, and 8 of them were successfully detected at distances of 45cm, 75cm, 105cm, and 135cm. For instance, segmentation, segmentation maps appeared clean when detecting a single object but faced challenges when multiple objects overlapped. |
| PDF File: | https://alife-robotics.co.jp/members2024/icarob/data/html/data/OS/OS23-5.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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