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9 January 2024 A deep learning approach for fruit detection: YOLO-GF
Jian Guo, Wei Wu
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Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129691E (2024) https://doi.org/10.1117/12.3014430
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China
Abstract
To achieve automatic fruit object recognition in complex backgrounds, this paper proposes a fruit object detection algorithm based on YOLO-GF. Addressing challenges such as complex backgrounds, significant variations in target shapes, and instances of occlusion in fruit images, we utilize the Global Attention Mechanism (GAM) to enhance the feature extraction capability for fruit targets, thereby improving fruit recognition accuracy. Additionally, the Focal-EIOU loss function is used instead of the CIOU loss function to expedite model convergence. Experimental results demonstrate a significant improvement in recognition accuracy under the same hardware conditions. On the same test dataset, the improved model achieves an mAP50 of 92.1% and mAP50:95 of 76.5%, representing increases of 5.8% and 11.9% compared to the original model, respectively.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Jian Guo and Wei Wu "A deep learning approach for fruit detection: YOLO-GF", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129691E (9 January 2024); https://doi.org/10.1117/12.3014430
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KEYWORDS
Object detection
Deep learning
Object recognition
Agriculture
Target recognition
Network architectures
Target detection
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