A lightweight tea buds terminal detection model based on YOLOv5s

doi:10.3969/j.issn.1004-1524.20230822

Acta Agriculturae Zhejiangensis ›› 2024, Vol. 36 ›› Issue (6): 1413-1424.DOI: 10.3969/j.issn.1004-1524.20230822

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A lightweight tea buds terminal detection model based on YOLOv5s

ZHU Mingmin¹(), ZHANG Guoping¹^,^*(), TAN Jianjun², SUN Lingjiao², ZHU Li¹^,³, JIAO Jie²

1. College of Physical Science and Technology, Central China Normal University, Wuhan 430079, China
2. College of Intelligent Engineering, Hubei Enshi College, Enshi 445000, Hubei,China
3. College of Intelligent Science and Engineering, Hubei Minzu University, Enshi 445000, Hubei, China

Received:2023-07-03 Online:2024-06-25 Published:2024-07-02

Abstract

Abstract:

Rapid and accurate identification of tea buds in tea garden environments is one of the key technologies for achieving intelligent tea picking. However, the complexity of the tea buds detection model leads to problems such as large model parameters, computational complexity, and model size, which limits the deployment of this model in embedded devices of tea picking robots. In view of this, this article proposes a lightweight tea buds terminal detection model based on YOLOv5s. Firstly, the lightweight network GhostNet is used to replace the Backbone network in YOLOv5s, and the Neck network is reconstructed to reduce the parameters, computation and memory consumption of the model. The improved model reduces 47.64%, 49.36% and 45.51% respectively. Secondly, by introducing a coordinated attention(CA) mechanism to suppress image background information, the model’s feature extraction ability for tea buds is enhanced. Next, multi-scale context (MSC) module is introduced into the Neck network to effectively fuse shallow image features and deep semantic features, which helps the network model extract effective recognition information. Then, the boundary box regression Loss function CIOU is replaced by EIOU to accelerate the Rate of convergence of the Loss function and improve the positioning accuracy of the tea buds boundary box. The experiment result shows that compared with the original YOLOv5s model, the improved model reduces the parameter count, computational complexity, and model memory usage by 3 Mb, 7.3 Gb, and 6.37 Mb, respectively, and improves detection accuracy by 0.3%. Finally, the model was transplanted to the Raspberry Pi platform through model transformation. After environmental deployment and inference engine acceleration, the lightweight model achieved the goal of detecting tea buds on Raspberry Pi with limited resources and computing power. It also improved the recognition accuracy of tea buds to a certain extent, providing theoretical research and technical support for the intelligent picking of tea buds.

Key words: tea bud detection, Raspberry Pie, lightweight, attention mechanism

CLC Number:

S24
TP391.4

ZHU Mingmin, ZHANG Guoping, TAN Jianjun, SUN Lingjiao, ZHU Li, JIAO Jie. A lightweight tea buds terminal detection model based on YOLOv5s[J]. Acta Agriculturae Zhejiangensis, 2024, 36(6): 1413-1424.

Figures/Tables 16

Fig.1 The original images and images after data enhancement

Fig.2 Structure diagram of YOLOv5s Conv2d, Two-dimensional convolution; BN, Batch normalization; SiLU, Activation function; CBS, Convolutional block; Bottleneck, Bottleneck structure; C3, Lightweight semantic segmentation network; SPPF, Fast spatial pyramid pooling module; Concat, Tensor splicing; Upsample, Upsampling; MaxPool, Maximum pooling.

Fig.3 Structure diagram of ghost module

Fig.4 Structure diagram of ghost bottleneck DWConv, Depth separable convolution; Stride, Step size.

Fig.5 Structure diagram of coordinate attention (CA) mechanism Residual, Residual module; AvgPooling, Average pooling; BatchNorm, Batch normalization; Sigmaid, Activation function; Re weight, Reweighting module.

Fig.6 Structure diagram of multi-scale context

Fig.7 Structure diagram of the improved YOLOv5s network Ghost CBS, Ghost convolutional block; Ghost C3, Ghost lightweight semantic segmentation network; DWConv, Depth separable convolution; CA, Attention mechanism; MSC, Multi-scale feature fusion module; Ghost Bottleneck, Ghost Bottleneck.

Fig.8 Photos of Raspberry Pie 4B

Fig.9 The execution process of ONNXRuntime

Table 1 Comparison of different lightweight models

模型 Models	参数量 Params/Mb	计算量 FLOPs/Gb	模型大小 Size/Mb	精准率 P/%	召回率 R/%	平均精度 AP/%
MobileNetV3	3.54	7.0	7.5	62.2	64.0	65.2
ShuffleNetV2	3.68	7.8	7.6	66.3	66.5	68.4
GhostNet	3.67	8.0	7.9	67.0	68.2	71.2

Table 2 Comparison of ablation test results

模型 Models	参数量 Params/Mb	计算量 FLOPs/Gb	模型大小 Size/Mb	精准率 P/%	召回率 R/%	平均精度 AP/%
YOLOv5s	7.01	15.8	14.50	68.8	67.5	72.7
A	3.67	8.0	7.90	67.0	68.2	71.2
B	3.74	8.1	8.00	67.5	68.4	72.0
C	3.98	8.5	8.13	68.4	68.5	72.5
D	4.01	8.5	8.13	68.5	68.8	73.0

Table 3 Comparison of different detection algorithm models

模型 Models	参数量 params/Mb	计算量 FLOPs/Gb	模型大小 size/Mb	精准率 P/%	召回率 R/%	平均精度 AP/%	t/s
Faster R-CNN^[28]	—	—	—	89.00	58.00	54.00	—
YOLOv3^[29]	—	—	—	74.51	69.56	71.96	—
Compact-YOLO v4^[30]	—	—	23.20	51.07	78.67	72.93	0.023
YOLOv5-Lite	1.54	3.70	3.40	60.30	61.70	60.00	0.063
YOLOv5s	7.01	15.8	14.50	68.80	67.50	72.70	0.014
Ours	4.01	8.50	8.13	68.50	68.80	73.00	0.016

Fig.10 Comparison of results before (A) and after (B) improvement of YOLOv5s model

Fig.11 Comparison of results before (A) and after (B) model improvement on Raspberry Pie

Fig.12 Comparison of results before (A) and after (B) ONNXRuntime inference

Table 4 Detection time of different models s

模型 Models	t₁	t₂	t₃	t₄
YOLOv5s	0.014	0.155	16.572	0.892
Ours	0.016	0.145	9.157	0.723

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A lightweight tea buds terminal detection model based on YOLOv5s

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