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

[1]	党亚男, 王芳, 田建艳, 等. 面向猪的姿态识别的特征优选方法研究[J]. 江苏农业科学, 2016, 44(3): 448-451.
	DANG Y N, WANG F, TIAN J Y, et al. Research on feature optimization method for pig pose recognition[J]. Jiangsu Agricultural Sciences, 2016, 44(3): 448-451. (in Chinese)
[2]	杨秋妹, 肖德琴, 张根兴. 猪只饮水行为机器视觉自动识别[J]. 农业机械学报, 2018, 49(6): 232-238.
	YANG Q M, XIAO D Q, ZHANG G X. Automatic pig drinking behavior recognition with machine vision[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018, 49(6): 232-238. (in Chinese with English abstract)
[3]	嵇杨培, 杨颖, 刘刚. 基于可见光光谱和YOLOv2的生猪饮食行为识别[J]. 光谱学与光谱分析, 2020, 40(5): 1588-1594.
	JI Y P, YANG Y, LIU G. Recognition of pig eating and drinking behavior based on visible spectrum and YOLOv2[J]. Spectroscopy and Spectral Analysis, 2020, 40(5): 1588-1594. (in Chinese with English abstract)
[4]	TRAULSEN I, SCHEEL C, AUER W, et al. Using acceleration data to automatically detect the onset of farrowing in sows[J]. Sensors (Basel, Switzerland), 2018, 18(1): 170. DOI URL
[5]	王凯, 刘春红, 段青玲. 基于MFO-LSTM的母猪发情行为识别[J]. 农业工程学报, 2020, 36(14): 211-219.
	WANG K, LIU C H, DUAN Q L. Identification of sow oestrus behavior based on MFO-LSTM[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020, 36(14): 211-219. (in Chinese with English abstract)
[6]	何屿彤, 李斌, 张锋, 等. 基于改进YOLOv3的猪脸识别[J]. 中国农业大学学报, 2021, 26(3): 53-62.
	HE Y T, LI B, ZHANG F, et al. Pig face recognition based on improved YOLOv3[J]. Journal of China Agricultural University, 2021, 26(3): 53-62. (in Chinese with English abstract)
[7]	焦俊, 王文周, 侯金波, 等. 基于改进残差网络的黑毛猪肉新鲜度识别方法[J]. 农业机械学报, 2019, 50(8): 364-371.
	JIAO J, WANG W Z, HOU J B, et al. Freshness identification of iberico pork based on improved residual network[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(8): 364-371. (in Chinese with English abstract)
[8]	邱洪涛, 孙裴, 侯金波, 等. 基于Caffe的猪肉新鲜度分级的设计与实现[J]. 江苏农业学报, 2019, 35(2): 461-468.
	QIU H T, SUN P, HOU J B, et al. Design and implementation of pork freshness grading based on Caffe[J]. Jiangsu Journal of Agricultural Sciences, 2019, 35(2): 461-468. (in Chinese with English abstract)
[9]	MARSOT M, MEI J Q, SHAN X C, et al. An adaptive pig face recognition approach using convolutional neural networks[J]. Computers and Electronics in Agriculture, 2020, 173: 105386. DOI URL
[10]	甘海明, 薛月菊, 李诗梅, 等. 基于时空信息融合的母猪哺乳行为识别[J]. 农业机械学报, 2020, 51(S1): 357-363.
	GAN H M, XUE Y J, LI S M, et al. Automatic sow nursing behaviour recognition based on spatio-temporal information fusion[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(S1): 357-363. (in Chinese with English abstract)
[11]	燕红文, 刘振宇, 崔清亮, 等. 基于改进Tiny-YOLO模型的群养生猪脸部姿态检测[J]. 农业工程学报, 2019, 35(18): 169-179.
	YAN H W, LIU Z Y, CUI Q L, et al. Detection of facial gestures of group pigs based on improved Tiny-YOLO[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(18): 169-179. (in Chinese with English abstract)
[12]	PSOTA E T, MITTEK M, PÉREZ L C, et al. Multi-pig part detection and association with a fully-convolutional network[J]. Sensors (Basel, Switzerland), 2019, 19(4): 852. DOI URL
[13]	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]// 2014 IEEE Conference on Computer Vision and Pattern Recognition.Columbus, OH, USA: IEEE, 2014: 580-587.
[14]	REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. DOI PMID
[15]	LIU W, ANGUELOV D, ERHAN D, et al. SSD: single shot MultiBox detector[J]. Lecture Notes in Computer Science, 2016, 9905: 21-37.
[16]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016: 779-788.
[17]	REDMON J, FARHADI A. YOLO9000: better, faster, stronger[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 6517-6525.
[18]	REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08) [2021-06-29]. https://arxiv.org/abs/1804.02767
[19]	BOCHKOVSKIY A, WANG C Y, LIAO H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. (2020-04-23) [2021-06-29]. https://arxiv.org/abs/2004.10934
[20]	HOWARD A, SANDLER M, CHEN B, et al. Searching for MobileNetV3[C]// 2019 IEEE/CVF International Conference on Computer Vision (ICCV). October 27-November 2, 2019. Seoul:IEEE, 2019: 1314-1324.
[21]	CHOLLET F. Xception: deep learning with depthwise separable convolutions[C]// 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE, 2017: 1800-1807.
[22]	SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Rethinking the inception architecture for computer vision[C]// 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE, 2016: 2818-2826.
[23]	WANG C Y, MARK LIAO H Y, WU Y H, et al. CSPNet: a new backbone that can enhance learning capability of CNN[C]// 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Seattle, WA, USA: IEEE, 2020: 1571-1580.
[24]	LIU S, QI L, QIN H F, et al. Path aggregation network for instance segmentation[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA: IEEE, 2018: 8759-8768.
[25]	HOWARD A G, ZHU M L, CHEN B, et al. MobileNets: efficient convolutional neural networks for mobile vision applications[EB/OL]. (2017-04-17) [2021-06-29]. https://arxiv.org/abs/1704.04861
[26]	SANDLER M, HOWARD A, ZHU M L, et al. MobileNetV2: inverted residuals and linear bottlenecks[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City, UT, USA. IEEE, 2018: 4510-4520.
[27]	TAN M X, CHEN B, PANG R M, et al. MnasNet: platform-aware neural architecture search for mobile[C]// 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Long Beach, CA, USA: IEEE, 2019: 2815-2823.
[28]	薛月菊, 朱勋沐, 郑婵, 等. 基于改进Faster R-CNN识别深度视频图像哺乳母猪姿态[J]. 农业工程学报, 2018, 34(9): 189-196.
	XUE Y J, ZHU X M, ZHENG C, et al. Lactating sow postures recognition from depth image of videos based on improved Faster R-CNN[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(9): 189-196. (in Chinese with English abstract)

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

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 14

References 28

Related Articles 1

Recommended Articles

Metrics

Comments

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