Pig posture detection based on improved YOLOv4 model

doi:10.3969/j.issn.1004-1524.2023.01.23

Acta Agriculturae Zhejiangensis ›› 2023, Vol. 35 ›› Issue (1): 215-225.DOI: 10.3969/j.issn.1004-1524.2023.01.23

• Biosystems Engineering • Previous Articles Next Articles

Pig posture detection based on improved YOLOv4 model

LI Bin¹(), LIU Dongyang¹, SHI Guolong¹, MU Jingsheng², XU Haoran¹, GU Lichuan¹, JIAO Jun¹^,^*()

1. School of Information and Computer, Anhui Agricultural University, Hefei 230036, China
2. Mengcheng Jinghuimeng Agricultural Science and Technology Development Co., Ltd., Bozhou 233524, Anhui, China

Received:2021-06-29 Online:2023-01-25 Published:2023-02-21

Abstract

Abstract:

In order to improve the speed and performance of pig posture detection in piggery environment, an improved Mini_YOLOv4 model was proposed in the present assay based on YOLOv4 model. Firstly, the feature extraction network of YOLOv4 was changed into lightweight MobileNetV3 network structure to reduce the amount of model parameters. Secondly, the deep separable convolution was used to replace the traditional convolution in the CBL_block1 and CBL_block2 modules of the detection network to avoid the memory shortage and high delay problems caused by the complex model. Finally, the 3×3 convolution at each scale of the original YOLOv4 network was changed into the Inception network structure to improve the accuracy of the model. The performance of the above models was evaluated in the detection of five types of postures of pigs, i.e. stand, sit, lie, ventral and lateral. It was shown that, compared with the YOLOv4 model, the detection accuracy of the proposed Mini_YOLOv4 model was improved by 4.01 percentage points, and its detection speed was almost doubled. In summary, the proposed model could improve the real-time performance and ensure the accuracy of pig posture detection, which could provide technical references for pig behavior identification.

Key words: YOLOv4 model, MobileNetV3 network, pig posture detection

CLC Number:

TP391.4

LI Bin, LIU Dongyang, SHI Guolong, MU Jingsheng, XU Haoran, GU Lichuan, JIAO Jun. Pig posture detection based on improved YOLOv4 model[J]. Acta Agriculturae Zhejiangensis, 2023, 35(1): 215-225.

Figures/Tables 14

References 28

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类型 Type	滤波器 Filters	Exp size	SE	激活函数 Activation function	步长 Stride	输出 Output
Conv2D	16	—	否No	HS	2	208×208
Bneck,3×3	16	16	否No	HS	1	208×208
Bneck,3×3	24	64	否No	RE	2	104×104
Bneck,3×3	24	72	否No	HS	1	104×104
Bneck,5×5	40	72	是Yes	RE	2	52×52
Bneck,5×5	40	120	是Yes	RE	1	52×52
Bneck,5×5	40	120	是Yes	RE	1	52×52
Bneck,3×3	80	240	否No	HS	2	26×26
Bneck,3×3	80	200	否No	HS	1	26×26
Bneck,3×3	80	184	否No	HS	1	26×26
Bneck,3×3	80	184	是Yes	HS	1	26×26
Bneck,3×3	112	480	是Yes	HS	1	26×26
Bneck,3×3	112	672	是Yes	HS	1	26×26
Bneck,5×5	160	672	是Yes	HS	2	13×13
Bneck,5×5	160	960	是Yes	HS	1	13×13
Bneck,5×5	160	960	否No	HS	1	13×13

类型 Type	滤波器 Filters	Exp size	SE	激活函数 Activation function	步长 Stride	输出 Output
Conv2D	16	—	否No	HS	2	208×208
Bneck,3×3	16	16	否No	HS	1	208×208
Bneck,3×3	24	64	否No	RE	2	104×104
Bneck,3×3	24	72	否No	HS	1	104×104
Bneck,5×5	40	72	是Yes	RE	2	52×52
Bneck,5×5	40	120	是Yes	RE	1	52×52
Bneck,5×5	40	120	是Yes	RE	1	52×52
Bneck,3×3	80	240	否No	HS	2	26×26
Bneck,3×3	80	200	否No	HS	1	26×26
Bneck,3×3	80	184	否No	HS	1	26×26
Bneck,3×3	80	184	是Yes	HS	1	26×26
Bneck,3×3	112	480	是Yes	HS	1	26×26
Bneck,3×3	112	672	是Yes	HS	1	26×26
Bneck,5×5	160	672	是Yes	HS	2	13×13
Bneck,5×5	160	960	是Yes	HS	1	13×13
Bneck,5×5	160	960	否No	HS	1	13×13

模型 Model	不同IoU阈值下的mAP mAP under different IoU thresholds
模型 Model	0.5	0.6	0.7	0.75
YOLOv4	69.66	66.71	58.34	52.94
Mini_YOLOv4	73.67	73.67	73.00	71.35

模型 Model	不同IoU阈值下的mAP mAP under different IoU thresholds
模型 Model	0.5	0.6	0.7	0.75
YOLOv4	69.66	66.71	58.34	52.94
Mini_YOLOv4	73.67	73.67	73.00	71.35

模型 Model	不同姿态下的检测精度Precision under different postures/%					检测速度 Speed/(frame·s^-1)
模型 Model	站立Stand	趴卧Lie	坐立Sit	腹卧Ventral	侧卧Lateral	检测速度 Speed/(frame·s^-1)
YOLOv4	66.17	69.25	71.15	64.82	76.89	33.98
Mini_YOLOv4	68.59	70.87	74.10	71.27	83.51	65.62

Pig posture detection based on improved YOLOv4 model

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Figures/Tables 14

References 28

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模型 Model	不同姿态下的召回率Recall under different postures/%					不同姿态下的F1值F1 under different postures/%
模型 Model	站立 Stand	趴卧 Lie	坐立 Sit	腹卧 Ventral	侧卧 Lateral	站立 Stand	趴卧 Lie	坐立 Sit	腹卧 Ventral	侧卧 Lateral
YOLOv4	66.67	70.26	73.78	71.22	84.54	0.78	0.78	0.79	0.75	0.78
Mini_YOLOv4	69.57	71.28	74.22	72.20	83.51	0.80	0.81	0.85	0.82	0.91

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