IO-YOLOv5: Improved Pig Detection under Various Illuminations and Heavy Occlusion

Jiajun Lai, Yun Liang (), Yingjie Kuang, Zhannan Xie, Hongyuan He, Yuxin Zhuo, Zekai Huang, Shijie Zhu and Zenghang Huang
Additional contact information
Jiajun Lai: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Yun Liang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Yingjie Kuang: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Zhannan Xie: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Hongyuan He: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Yuxin Zhuo: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Zekai Huang: College of Engineering, South China Agricultural University, Guangzhou 510642, China
Shijie Zhu: College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Zenghang Huang: College of Engineering, South China Agricultural University, Guangzhou 510642, China

Agriculture, 2023, vol. 13, issue 7, 1-18

Abstract: Accurate detection and counting of live pigs are integral to scientific breeding and production in intelligent agriculture. However, existing pig counting methods are challenged by heavy occlusion and varying illumination conditions. To overcome these challenges, we proposed IO-YOLOv5 (Illumination-Occlusion YOLOv5), an improved network that expands on the YOLOv5 framework with three key contributions. Firstly, we introduced the Simple Attention Receptive Field Block (SARFB) module to expand the receptive field and give greater weight to important features at different levels. The Ghost Spatial Pyramid Pooling Fast Cross Stage Partial Connections (GSPPFC) module was also introduced to enhance model feature reuse and information flow. Secondly, we optimized the loss function by using Varifocal Loss to improve the model’s learning ability on high-quality and challenging samples. Thirdly, we proposed a public dataset consisting of 1270 images and 15,672 pig labels. Experiments demonstrated that IO-YOLOv5 achieved a mean average precision (mAP) of 90.8% and a precision of 86.4%, surpassing the baseline model by 2.2% and 3.7% respectively. By using a model ensemble and test time augmentation, we further improved the mAP to 92.6%, which is a 4% improvement over the baseline model. Extensive experiments showed that IO-YOLOv5 exhibits excellent performance in pig recognition, particularly under heavy occlusion and various illuminations. These results provide a strong foundation for pig recognition in complex breeding environments.

Keywords: object detection; live pig; SARFB; GSPPFC; Varifocal Loss; heavy occlusion; various illumination (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/13/7/1349/pdf (application/pdf)
https://www.mdpi.com/2077-0472/13/7/1349/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:13:y:2023:i:7:p:1349-:d:1186502

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().