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Small target tea bud detection based on improved YOLOv5 in complex background

文献类型： 外文期刊

作者： Wang, Mengjie 1 ; Li, Yang 2 ; Meng, Hewei 1 ; Chen, Zhiwei 2 ; Gui, Zhiyong 2 ; Li, Yaping 1 ; Dong, Chunwang 3 ;

作者机构： 1.Shihezi Univ, Coll Mech & Elect Engn, Shihezi, Peoples R China

2.Chinese Acad Agr Sci, Tea Res Inst, Key Lab Tea Qual & Safety Control, Minist Agr & Rural Affairs, Hangzhou 310008, Peoples R China

3.Shandong Acad Agr Sci, Tea Res Inst, Jinan 250100, Peoples R China

关键词： object detection; deep information extraction; lightweight; MPDIoU; YOLOv5; attention mechanism

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.1； 五年影响因子：5.3 ）

ISSN： 1664-462X

年卷期： 2024 年 15 卷

页码：

收录情况： SCI

摘要： Tea bud detection is the first step in the precise picking of famous teas. Accurate and fast tea bud detection is crucial for achieving intelligent tea bud picking. However, existing detection methods still exhibit limitations in both detection accuracy and speed due to the intricate background of tea buds and their small size. This study uses YOLOv5 as the initial network and utilizes attention mechanism to obtain more detailed information about tea buds, reducing false detections and missed detections caused by different sizes of tea buds; The addition of Spatial Pyramid Pooling Fast (SPPF) in front of the head to better utilize the attention module's ability to fuse information; Introducing the lightweight convolutional method Group Shuffle Convolution (GSConv) to ensure model efficiency without compromising accuracy; The Mean-Positional-Distance Intersection over Union (MPDIoU) can effectively accelerate model convergence and reduce the training time of the model. The experimental results demonstrate that our proposed method achieves precision (P), recall rate (R) and mean average precision (mAP) of 93.38%, 89.68%, and 95.73%, respectively. Compared with the baseline network, our proposed model's P, R, and mAP have been improved by 3.26%, 11.43%, and 7.68%, respectively. Meanwhile, comparative analyses with other deep learning methods using the same dataset underscore the efficacy of our approach in terms of P, R, mAP, and model size. This method can accurately detect the tea bud area and provide theoretical research and technical support for subsequent tea picking.

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