Version 1
: Received: 10 September 2024 / Approved: 10 September 2024 / Online: 13 September 2024 (14:55:17 CEST)
How to cite:
Yılmaz, A.; Yurtay, Y.; Yurtay, N. Development Of A Real-Time Object Detection Model For The Detection Of Secretly Cultivated Plants. Preprints2024, 2024091093. https://doi.org/10.20944/preprints202409.1093.v1
Yılmaz, A.; Yurtay, Y.; Yurtay, N. Development Of A Real-Time Object Detection Model For The Detection Of Secretly Cultivated Plants. Preprints 2024, 2024091093. https://doi.org/10.20944/preprints202409.1093.v1
Yılmaz, A.; Yurtay, Y.; Yurtay, N. Development Of A Real-Time Object Detection Model For The Detection Of Secretly Cultivated Plants. Preprints2024, 2024091093. https://doi.org/10.20944/preprints202409.1093.v1
APA Style
Yılmaz, A., Yurtay, Y., & Yurtay, N. (2024). Development Of A Real-Time Object Detection Model For The Detection Of Secretly Cultivated Plants. Preprints. https://doi.org/10.20944/preprints202409.1093.v1
Chicago/Turabian Style
Yılmaz, A., Yüksel Yurtay and Nilüfer Yurtay. 2024 "Development Of A Real-Time Object Detection Model For The Detection Of Secretly Cultivated Plants" Preprints. https://doi.org/10.20944/preprints202409.1093.v1
Abstract
AYOLO differentiates itself with a new fusion architecture proposal that leverages the strengths of unsupervised learning and integrates the Vision Transformer approach, taking the YOLO series models as a reference. This innovation enables the model to effectively use rich, unlabeled data, providing a new example in pre-training methodology for YOLO architectures. On the benchmark COCO val2017 dataset, AYOLO demonstrates its superiority by achieving an impressive 38.7 % AP while maintaining an outstanding rendering speed of 239 FPS (Frame Per Second) on the Tesla K80 GPU. This performance outperforms the previous state-of-the-art YOLO v6-3.0 by a significant margin of +2.2 % AP while maintaining comparable FPS. AYOLO is presented as a demonstration of the potential of integrating complex information fusion techniques with supervised pretraining in improving the precision and speed of object detection models.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
