A lightweight bumblebee image classification model based on improved GhostNet V2

doi:10.3969/j.issn.1004-1524.20240190

Acta Agriculturae Zhejiangensis ›› 2024, Vol. 36 ›› Issue (12): 2832-2845.DOI: 10.3969/j.issn.1004-1524.20240190

• Biosystems Engineering • Previous Articles Next Articles

A lightweight bumblebee image classification model based on improved GhostNet V2

FAN Weipei¹^,²^,³(), YU Xiaoming¹, SHEN Fenglong¹^,^*(), WANG Liang²^,³, WANG Xing⁴

1. School of Information Engineering, Liaodong University, Dandong 118003, Liaoning, China
2. School of Computer Science and Technology, Shenyang University of Chemical Technology, Shenyang 110142, China
3. Key Laboratory of Chemical Process Industry Intelligent Technology of Liaoning Province, Shenyang University of Chemical Technology, Shenyang 110142, China
4. School of Agriculture, Liaodong University, Dandong 118003, Liaoning, China

Received:2024-03-01 Online:2024-12-25 Published:2024-12-27

Abstract

Abstract:

In order to realize automatic sorting of bumblebees accurately and quickly, a lightweight deep learning bumblebee image classification model is proposed. Firstly, 1 742 images of queen, drone and worker bees of ground bumblebee were collected, and a dataset containing 13 117 bumblebee images was constructed by data augmentation. Then, based on the GhostNet V2 model, the feature information of the input image under more receptive fields was obtained by multi-scale convolution, two shortcut branches were added to fuse the features of the low-level and the middle-level, the high-level respectively, the ReLU activation function was replaced by SiLU, and the number of bottleneck layers and channels was cut, and a lightweight bumblebee image classification model GMCFF was designed. The results showed that the classification accuracy of the BumblebeeImage dataset using the GMCFF model reached 98.40%, which was 1.53 percentage points higher than the original model, and the classification accuracy compared with the ShuffleNetV2 and MobileNetV2 model was also higher, increasing by 1.53 and 1.15 percentage points respectively. The number of parameters of the model was only 0.73 M, the amount of floating point operations was decreased by 25.15 M compared with that before the improvement, the model size was only 3.01 MB, and the average test time of a single bumblebee image was 17.08 ms, which met the requirements of lightweight and real-time.

Key words: bumblebee classification, GhostNet V2, lightweight, multi-scale convolution, feature fusion

CLC Number:

TP391.4

FAN Weipei, YU Xiaoming, SHEN Fenglong, WANG Liang, WANG Xing. A lightweight bumblebee image classification model based on improved GhostNet V2[J]. Acta Agriculturae Zhejiangensis, 2024, 36(12): 2832-2845.

Figures/Tables 22

References 25

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熊蜂类别 Bumblebee category	训练集 Training set	验证集 Validation set	测试集 Test set	总计 Total
蜂王Queen bee	3 011	861	430	4 302
雄蜂Drone bee	3 130	895	447	4 472
工蜂Worker bee	3 040	869	434	4 343
总计Total	9 181	2 625	1 311	13 117

熊蜂类别 Bumblebee category	训练集 Training set	验证集 Validation set	测试集 Test set	总计 Total
蜂王Queen bee	3 011	861	430	4 302
雄蜂Drone bee	3 130	895	447	4 472
工蜂Worker bee	3 040	869	434	4 343
总计Total	9 181	2 625	1 311	13 117

输入Input	操作Operator	增大比例Exp	输出通道数Out	通道注意力SE	步长Stride
224×224×3	Conv2d 3×3、5×5、7×7	—	16	—	2
122×122×16	GV2-bneck	16	16	—	1
122×122×16	GV2-bneck	48	24	—	2
56×56×24	GV2-bneck	72	24	—	1
56×56×24	GV2-bneck	72	40	0.25	2
28×28×40	GV2-bneck	120	40	0.25	1
28×28×40	GV2-bneck	240	80	—	2
14×14×80	GV2-bneck	200	80	—	1
14×14×80	GV2-bneck	184	112	0.25	1
14×14×112	GV2-bneck	672	112	0.25	1
14×14×112	GV2-bneck	160	160	0.25	2
7×7×160	Conv2d 1×1	—	160	—	1
7×7×160	AvgPool 7×7	—	480	—	-
1×1×480	Conv2d 1×1	—	480	—	1
1×1×480	FC	—	3	—	-

输入Input	操作Operator	增大比例Exp	输出通道数Out	通道注意力SE	步长Stride
224×224×3	Conv2d 3×3、5×5、7×7	—	16	—	2
122×122×16	GV2-bneck	16	16	—	1
122×122×16	GV2-bneck	48	24	—	2
56×56×24	GV2-bneck	72	24	—	1
56×56×24	GV2-bneck	72	40	0.25	2
28×28×40	GV2-bneck	120	40	0.25	1
28×28×40	GV2-bneck	240	80	—	2
14×14×80	GV2-bneck	200	80	—	1
14×14×80	GV2-bneck	184	112	0.25	1
14×14×112	GV2-bneck	672	112	0.25	1
14×14×112	GV2-bneck	160	160	0.25	2
7×7×160	Conv2d 1×1	—	160	—	1
7×7×160	AvgPool 7×7	—	480	—	-
1×1×480	Conv2d 1×1	—	480	—	1
1×1×480	FC	—	3	—	-

数据增强 Data augmentation	准确率 Accuracy	精确率 Precision	召回率 Recall	F1分数 F1-score
未使用Unused	82.53	83.99	82.49	82.83
使用Used	98.40	98.42	98.41	98.41

A lightweight bumblebee image classification model based on improved GhostNet V2

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激活函数 Activation function	准确率 Accuracy	精确率 Precision	召回率 Recall	F1分数 F1-score
ReLU	97.48	97.49	97.49	97.49
LeakyReLU	97.48	97.51	97.50	97.50
H-swish	97.48	97.49	97.49	97.49
SiLU	98.40	98.42	98.41	98.41

模型 Model	改进方式Improvement mode			准确率 Accuracy/%	F1分数 F1- score/%	参数量 Parameters/ M	浮点运算量 FLOPs/M
模型 Model	多尺度卷积 Multi-scale convolution	特征融合 Feature fusion	架构精简 Streamlined architecture	准确率 Accuracy/%	F1分数 F1- score/%	参数量 Parameters/ M	浮点运算量 FLOPs/M
GhostNet V2	×	×	×	96.87	96.89	4.88	184.50
	√	×	×	98.02	98.03	5.03	229.06
	×	√	×	97.56	97.57	4.90	194.82
	×	×	√	96.49	96.50	0.71	103.54
	√	√	×	97.78	97.80	5.06	239.38
	√	×	√	97.25	97.26	0.82	148.10
	×	√	√	96.57	96.58	0.75	113.86
GMCFF	√	√	√	98.40	98.41	0.73	159.35

模型 Model	准确率 Accuracy/%	精确率 Precision/%	召回率 Recall/%	F1分数 F1-score/%	参数量 Parameters/M	浮点运算量 FLOPs/M	模型大小 Model size/MB
DenseNet121	97.71	97.74	97.71	97.72	6.96	2 895.99	27.1
RegNet	94.66	94.68	94.69	94.68	2.32	207.34	9.00
ShuffleNetV2	96.87	96.90	96.89	96.89	1.26	151.69	4.95
MobileNetV2	97.25	97.29	97.26	97.26	2.23	326.27	8.73
MobileNetV3_small	96.26	96.36	96.30	96.29	1.52	61.17	5.92
ResNet18	97.03	97.04	97.05	97.04	11.18	1 823.52	42.7
EfficientNetB0	97.64	97.65	97.64	97.65	4.01	411.55	15.5
GhostNet	97.25	97.26	97.27	97.26	3.91	154.58	15.1
GhostNetV2	96.87	96.90	96.90	96.89	4.88	184.50	19.1
GMCFF	98.40	98.42	98.41	98.41	0.73	159.35	3.01

模型 Model	准确率 Accuracy/%	精确率 Precision/%	召回率 Recall/%	F1分数 F1-score/%	参数量 Parameters/M	浮点运算量 FLOPs/M	模型大小 Model size/MB
GhostNet V2	93.90	93.92	93.92	93.92	4.88	184.50	19.1
GMCFF	93.94	93.97	93.96	93.96	0.73	159.35	3.01

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