发表机构
Bowling Green State University; South Dakota State University West River Research and Extension Center（博林格林州立大学; 南达科他州立大学西河研究与推广中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究评估YOLOv11和YOLOv26结合类平衡策略（RMO和SMOTE）自动检测牛红眼病，RMO-s在YOLOv11上达到0.99准确率、1.00真阳性率，证明该方法在牲畜健康监测中的潜力。
AI 中文摘要
传染性牛红眼病是一种传染性眼部疾病，对牛的健康、福利和农业生产率产生不利影响。传统诊断主要依赖临床观察，这可能主观、耗时，并且在大规模牛群或偏远环境中难以有效实施。本研究评估并比较了You Only Look Once (YOLO) v11和YOLOv26在自动化牛红眼病分类中的表现，并探讨了类平衡策略对模型性能的影响。每种架构的五个变体（n、s、m、l和x）使用原始不平衡数据集、随机少数类过采样（RMO）和改编的合成少数类过采样技术（SMOTE）进行训练和评估。YOLOv11和YOLOv26均表现出强大的分类性能，尽管类平衡的效果在不同模型变体间有所不同。对于YOLOv11，RMO-s实现了0.99的准确率、0.98的宏F1分数和1.00的真阳性率（TPR），且无假阴性分类。RMO-m也实现了1.00的TPR且无假阴性。对于YOLOv26，原始l、RMO-m和RMO-l变体均实现了0.99的准确率和0.98的宏F1分数，其中RMO-l达到了1.00的TPR且无假阴性。总体而言，RMO在少数类检测方面通常比改编的SMOTE提供更大的改进，而原始YOLOv26-l的强劲表现表明并非所有模型变体都需要过采样。这些发现证明了YOLOv11和YOLOv26在自动化检测牛红眼病方面的潜力，并支持在牲畜健康监测中进一步评估。
英文摘要
Infectious bovine pinkeye is a contagious ocular disease that adversely affects cattle health, welfare, and agricultural productivity. Conventional diagnosis relies primarily on clinical observation, which can be subjective, time-consuming, and difficult to implement efficiently in large herds or remote settings. This study evaluated and compared You Only Look Once (YOLO) v11 and YOLOv26 for automated bovine pinkeye classification and investigated the effects of class-balancing strategies on model performance. Five variants (n, s, m, l, and x) of each architecture were trained and evaluated using the original imbalanced dataset, Random Minority Oversampling (RMO), and an adapted Synthetic Minority Oversampling Technique (SMOTE). Both YOLOv11 and YOLOv26 demonstrated strong classification performance, although the effects of class balancing varied across model variants. For YOLOv11, RMO-s achieved an accuracy of 0.99, a macro F1-score of 0.98, and a true positive rate (TPR) of 1.00, with no false-negative classifications. RMO-m also achieved a TPR of 1.00 with no false negatives. For YOLOv26, the original l, RMO-m, and RMO-l variants each achieved an accuracy of 0.99 and a macro F1-score of 0.98, with RMO-l attaining a TPR of 1.00 and no false negatives. Overall, RMO generally provided greater improvements in minority-class detection than adapted SMOTE, whereas the strong performance of the original YOLOv26-l demonstrates that oversampling was not necessary for all model variants. These findings demonstrate the potential of YOLOv11 and YOLOv26 for automated detection of bovine pinkeye and support further evaluation for livestock health monitoring.
