Lightweight tea bud recognition network integrating GhostNet and YOLOv5

Miaolong Cao; Hao Fu; Jiayi Zhu; Chenggang Cai; Miaolong Cao; Hao Fu; Jiayi Zhu; Chenggang Cai

doi:10.3934/mbe.2022602

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 12: 12897-12914. doi: 10.3934/mbe.2022602

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Research article Special Issues

Lightweight tea bud recognition network integrating GhostNet and YOLOv5

1.
School of Mechanical and Energy Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
2.
School of Biological and Chemical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China

Academic Editor: Vladimir Mityushev

Received: 12 July 2022 Revised: 19 August 2022 Accepted: 29 August 2022 Published: 05 September 2022

Aiming at the problems of low detection accuracy and slow speed caused by the complex background of tea sprouts and the small target size, this paper proposes a tea bud detection algorithm integrating GhostNet and YOLOv5. To reduce parameters, the GhostNet module is specially introduced to shorten the detection speed. A coordinated attention mechanism is then added to the backbone layer to enhance the feature extraction ability of the model. A bi-directional feature pyramid network (BiFPN) is used in the neck layer of feature fusion to increase the fusion between shallow and deep networks to improve the detection accuracy of small objects. Efficient intersection over union (EIOU) is used as a localization loss to improve the detection accuracy in the end. The experimental results show that the precision of GhostNet-YOLOv5 is 76.31%, which is 1.31, 4.83, and 3.59% higher than that of Faster RCNN, YOLOv5 and YOLOv5-Lite respectively. By comparing the actual detection effects of GhostNet-YOLOv5 and YOLOv5 algorithm on buds in different quantities, different shooting angles, and different illumination angles, and taking F1 score as the evaluation value, the results show that GhostNet-YOLOv5 is 7.84, 2.88, and 3.81% higher than YOLOv5 algorithm in these three different environments.
- YOLOv5,
- tea bud detection,
- deep learning,
- GhostNet,
- coordinate attention
Citation: Miaolong Cao, Hao Fu, Jiayi Zhu, Chenggang Cai. Lightweight tea bud recognition network integrating GhostNet and YOLOv5[J]. Mathematical Biosciences and Engineering, 2022, 19(12): 12897-12914. doi: 10.3934/mbe.2022602

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Abstract

Aiming at the problems of low detection accuracy and slow speed caused by the complex background of tea sprouts and the small target size, this paper proposes a tea bud detection algorithm integrating GhostNet and YOLOv5. To reduce parameters, the GhostNet module is specially introduced to shorten the detection speed. A coordinated attention mechanism is then added to the backbone layer to enhance the feature extraction ability of the model. A bi-directional feature pyramid network (BiFPN) is used in the neck layer of feature fusion to increase the fusion between shallow and deep networks to improve the detection accuracy of small objects. Efficient intersection over union (EIOU) is used as a localization loss to improve the detection accuracy in the end. The experimental results show that the precision of GhostNet-YOLOv5 is 76.31%, which is 1.31, 4.83, and 3.59% higher than that of Faster RCNN, YOLOv5 and YOLOv5-Lite respectively. By comparing the actual detection effects of GhostNet-YOLOv5 and YOLOv5 algorithm on buds in different quantities, different shooting angles, and different illumination angles, and taking F1 score as the evaluation value, the results show that GhostNet-YOLOv5 is 7.84, 2.88, and 3.81% higher than YOLOv5 algorithm in these three different environments.

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