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Recognition Method of Cabbage Heads at Harvest Stage under Complex Background Based on Improved YOLOv8n

文献类型： 外文期刊

作者： Tian, Yongqiang 1 ; Zhao, Chunjiang 2 ; Zhang, Taihong 1 ; Wu, Huarui 2 ; Zhao, Yunjie 1 ;

作者机构： 1.Xinjiang Agr Univ, Sch Comp & Informat Engn, Urumqi 830052, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100125, Peoples R China

3.Minist Educ, Engn Res Ctr Intelligent Agr, Urumqi 830052, Peoples R China

4.Xinjiang Agr Informatizat Engn Technol Res Ctr, Urumqi 830052, Peoples R China

5.Minist Agr & Rural Affairs, Key Lab Digital Village Technol, Beijing 100125, Peoples R China

关键词： cabbage; recognition and localization; object detection; deep learning; automatic harvesting

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.3； 五年影响因子：3.5 ）

ISSN：

年卷期： 2024 年 14 卷 7 期

页码：

收录情况： SCI

摘要： To address the problems of low recognition accuracy and slow processing speed when identifying harvest-stage cabbage heads in complex environments, this study proposes a lightweight harvesting period cabbage head recognition algorithm that improves upon YOLOv8n. We propose a YOLOv8n-Cabbage model, integrating an enhanced backbone network, the DyHead (Dynamic Head) module insertion, loss function optimization, and model light-weighting. To assess the proposed method, a comparison with extant mainstream object detection models is conducted. The experimental results indicate that the improved cabbage head recognition model proposed in this study can adapt cabbage head recognition under different lighting conditions and complex backgrounds. With a compact size of 4.8 MB, this model achieves 91% precision, 87.2% recall, and a mAP@50 of 94.5%-the model volume has been reduced while the evaluation metrics have all been improved over the baseline model. The results demonstrate that this model can be applied to the real-time recognition of harvest-stage cabbage heads under complex field environments.

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