School of Information and Computer, Anhui Agricultural University, Hefei 230036, China.
Key Laboratory of Agricultural Sensors, Ministry of Agriculture and Rural Affairs, Hefei 230036, China.
Sensors (Basel). 2023 May 31;23(11):5242. doi: 10.3390/s23115242.
Individual identification of pigs is a critical component of intelligent pig farming. Traditional pig ear-tagging requires significant human resources and suffers from issues such as difficulty in recognition and low accuracy. This paper proposes the YOLOv5-KCB algorithm for non-invasive identification of individual pigs. Specifically, the algorithm utilizes two datasets-pig faces and pig necks-which are divided into nine categories. Following data augmentation, the total sample size was augmented to 19,680. The distance metric used for K-means clustering is changed from the original algorithm to 1-IOU, which improves the adaptability of the model's target anchor boxes. Furthermore, the algorithm introduces SE, CBAM, and CA attention mechanisms, with the CA attention mechanism being selected for its superior performance in feature extraction. Finally, CARAFE, ASFF, and BiFPN are used for feature fusion, with BiFPN selected for its superior performance in improving the detection ability of the algorithm. The experimental results indicate that the YOLOv5-KCB algorithm achieved the highest accuracy rates in pig individual recognition, surpassing all other improved algorithms in average accuracy rate (IOU = 0.5). The accuracy rate of pig head and neck recognition was 98.4%, while the accuracy rate for pig face recognition was 95.1%, representing an improvement of 4.8% and 13.8% over the original YOLOv5 algorithm. Notably, the average accuracy rate of identifying pig head and neck was consistently higher than pig face recognition across all algorithms, with YOLOv5-KCB demonstrating an impressive 2.9% improvement. These results emphasize the potential for utilizing the YOLOv5-KCB algorithm for precise individual pig identification, facilitating subsequent intelligent management practices.
个体猪只识别是智能养猪的关键组成部分。传统的猪耳标需要大量的人力资源，并且存在识别困难和准确率低等问题。本文提出了一种用于个体猪只非侵入式识别的 YOLOv5-KCB 算法。具体来说，该算法利用了猪脸和猪颈两个数据集，将其分为九类。在进行数据增强后，总样本量增加到 19680 个。用于 K-means 聚类的距离度量从原始算法的 0.1-IOU 更改为 1-IOU，提高了模型目标锚框的适应性。此外，该算法引入了 SE、CBAM 和 CA 注意力机制，CA 注意力机制在特征提取方面表现出色。最后，使用 CARAFE、ASFF 和 BiFPN 进行特征融合，BiFPN 因其在提高算法检测能力方面的优异表现而被选中。实验结果表明，YOLOv5-KCB 算法在个体猪识别方面取得了最高的准确率，在平均准确率（IOU=0.5）方面超过了所有其他改进算法。猪头颈识别的准确率为 98.4%，猪脸识别的准确率为 95.1%，分别比原始的 YOLOv5 算法提高了 4.8%和 13.8%。值得注意的是，在所有算法中，猪头颈识别的平均准确率始终高于猪脸识别，而 YOLOv5-KCB 算法的准确率提高了 2.9%，表现尤为突出。这些结果强调了利用 YOLOv5-KCB 算法进行精确个体猪只识别的潜力，为后续的智能管理实践提供了便利。
