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DOI: 10.14569/IJACSA.2023.0140840
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Detection of Herd Pigs Based on Improved YOLOv5s Model

Author 1: Jianquan LI
Author 2: Xiao WU
Author 3: Yuanlin NING
Author 4: Ying YANG
Author 5: Gang LIU
Author 6: Yang MI

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 8, 2023.

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Abstract: Fast and accurate detection technology for individual pigs raised in herds is crucial for subsequent research on counting and disease surveillance. In this paper, we propose an improved lightweight object detection method based on YOLOv5s to improve the speed and accuracy of detection of herd-raised pigs in real-world and complex environments. Specifically, we first introduce a lightweight feature extraction module called C3S, then replace the original large object detection layer with a small object detection layer at the output (head) of YOLOv5s. Finally, we propose a dual adaptive weighted PAN structure to compensate for the information loss of feature map at the neck of YOLOv5s caused by down sampling. Experiments show that our method has an accuracy rate of 95.2%, a recall rate of 89.1%, a mean Average Precision (mAP) of 95.3%, a model parameter number of 3.64M, a detection speed of 154 frames per second, and a model layer count of 183 layers. Comparing with the original YOLOv5s model and the current state-of-the-art object detection models, our proposed method achieves the best results in terms of mAP and detection speed.

Keywords: Pig; deep learning; computer vision; object detection

Jianquan LI, Xiao WU, Yuanlin NING, Ying YANG, Gang LIU and Yang MI, “Detection of Herd Pigs Based on Improved YOLOv5s Model” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140840

@article{LI2023,
title = {Detection of Herd Pigs Based on Improved YOLOv5s Model},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140840},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140840},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Jianquan LI and Xiao WU and Yuanlin NING and Ying YANG and Gang LIU and Yang MI}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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