Meng Yao, Yoon Sook, Han Shujie, Fuentes Alvaro, Park Jongbin, Jeong Yongchae, Park Dong Sun
Department of Electronic Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea.
Core Research Institute of Intelligent Robots, Jeonbuk National University, Jeonju 54896, Republic of Korea.
Animals (Basel). 2023 Nov 20;13(22):3588. doi: 10.3390/ani13223588.
Accurate identification of individual cattle is of paramount importance in precision livestock farming, enabling the monitoring of cattle behavior, disease prevention, and enhanced animal welfare. Unlike human faces, the faces of most Hanwoo cattle, a native breed of Korea, exhibit significant similarities and have the same body color, posing a substantial challenge in accurately distinguishing between individual cattle. In this study, we sought to extend the closed-set scope (only including identifying known individuals) to a more-adaptable open-set recognition scenario (identifying both known and unknown individuals) termed Cattle's Face Open-Set Recognition (CFOSR). By integrating open-set techniques to enhance the closed-set accuracy, the proposed method simultaneously addresses the open-set scenario. In CFOSR, the objective is to develop a trained model capable of accurately identifying known individuals, while effectively handling unknown or novel individuals, even in cases where the model has been trained solely on known individuals. To address this challenge, we propose a novel approach that integrates Adversarial Reciprocal Points Learning (ARPL), a state-of-the-art open-set recognition method, with the effectiveness of Additive Margin Softmax loss (AM-Softmax). ARPL was leveraged to mitigate the overlap between spaces of known and unknown or unregistered cattle. At the same time, AM-Softmax was chosen over the conventional Cross-Entropy loss (CE) to classify known individuals. The empirical results obtained from a real-world dataset demonstrated the effectiveness of the ARPL and AM-Softmax techniques in achieving both intra-class compactness and inter-class separability. Notably, the results of the open-set recognition and closed-set recognition validated the superior performance of our proposed method compared to existing algorithms. To be more precise, our method achieved an AUROC of 91.84 and an OSCR of 87.85 in the context of open-set recognition on a complex dataset. Simultaneously, it demonstrated an accuracy of 94.46 for closed-set recognition. We believe that our study provides a novel vision to improve the classification accuracy of the closed set. Simultaneously, it holds the potential to significantly contribute to herd monitoring and inventory management, especially in scenarios involving the presence of unknown or novel cattle.
在精准畜牧养殖中，准确识别每头牛至关重要，这有助于监测牛的行为、预防疾病并提高动物福利。与人类面部不同，韩国本土品种韩牛的大多数面部表现出显著的相似性，且身体颜色相同，这给准确区分个体牛带来了巨大挑战。在本研究中，我们试图将封闭集范围（仅包括识别已知个体）扩展到更具适应性的开放集识别场景（识别已知和未知个体），即牛脸开放集识别（CFOSR）。通过整合开放集技术来提高封闭集的准确性，所提出的方法同时解决了开放集场景问题。在CFOSR中，目标是开发一个经过训练的模型，该模型能够准确识别已知个体，同时有效地处理未知或新出现的个体，即使该模型仅在已知个体上进行了训练。为应对这一挑战，我们提出了一种新颖的方法，该方法将最先进的开放集识别方法对抗性互易点学习（ARPL）与加法边际Softmax损失（AM-Softmax）的有效性相结合。利用ARPL来减轻已知牛与未知或未注册牛的空间之间的重叠。同时，选择AM-Softmax而非传统的交叉熵损失（CE）来对已知个体进行分类。从真实世界数据集获得的实证结果证明了ARPL和AM-Softmax技术在实现类内紧凑性和类间可分离性方面的有效性。值得注意的是，开放集识别和封闭集识别的结果验证了我们提出的方法相对于现有算法的优越性能。更确切地说，在复杂数据集的开放集识别背景下，我们的方法实现了91.84的曲线下面积（AUROC）和87.85的开放集识别率（OSCR）。同时，它在封闭集识别方面展示了94.46的准确率。我们相信我们的研究为提高封闭集的分类准确性提供了新的视角。同时，它有可能对畜群监测和库存管理做出重大贡献，特别是在涉及未知或新出现牛的场景中。
