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27 September 2024 An application of improved YOLOv8 in cherry recognition in natural environment
Shuai Gao, Xianjin Chen, Ran Peng, Xingchen Lan, Zitong Qiu, Yue Peng, Bo Wang, Tao Liu
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Proceedings Volume 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024); 132811C (2024) https://doi.org/10.1117/12.3051236
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning, 2024, Zhengzhou, China
Abstract
To overcome challenges related to occlusion and varying lighting conditions in cherry detection, we propose an enhanced YOLOv8-based model for cherry recognition. We have modified the YOLOv8 model’s ‘neck’ section by incorporating a BiFPN-based pyramidal network, which enhances feature fusion across various levels, thereby improving the model’s ability to identify partially obscured cherries. Additionally, to enhance adaptability to diverse lighting and to better detect small targets, we utilize an AFPN to refine the detection head, thereby improving the model’s performance across varying lighting scenarios. For training and validation purposes, we created a custom dataset of cherries that includes instances of occlusion and complex lighting. Our experimental findings indicate that the enhanced algorithm outperforms the original YOLOv8 network, with improvements in accuracy, recall, and mean Average Precision (mAP) by 4.2%, 2.6%, and 2.3%, respectively. Notably, the improved model demonstrates greater robustness in handling occluded cherries and complex lighting situations. The model achieves high recognition accuracy and robustness, offering valuable insights for future research in cherry recognition.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Shuai Gao, Xianjin Chen, Ran Peng, Xingchen Lan, Zitong Qiu, Yue Peng, Bo Wang, and Tao Liu "An application of improved YOLOv8 in cherry recognition in natural environment", Proc. SPIE 13281, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2024), 132811C (27 September 2024); https://doi.org/10.1117/12.3051236
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KEYWORDS
Feature fusion
Detection and tracking algorithms
Head
Target detection
Light sources and illumination
Neck
Performance modeling
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