使用YOLO26实现最优腺病毒检测
Toward Optimal Adenovirus Detection Using YOLO26
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中文总结 AI 辅助
研究针对腺病毒检测，在YOLO26模型不同大小变体下系统测试NAS、GAS、GMAS和DAS等数据增强设置，通过重新标注腺病毒数据集生成兼容标注，实验确定了最有效的数据增强设置以用于腺病毒检测。
中文摘要 AI 辅助
本研究系统地对YOLO26模型大小变体的不同数据增强设置进行基准测试，以确定在透射电子显微镜（TEM）图像中检测腺病毒的最有效设置。基准设置包括NAS、GAS、GMAS和DAS，均在相同训练条件下评估。从已发布的TEM病毒数据集中选择腺病毒数据集，利用腺病毒颗粒位置重新标注，生成与YOLO兼容的边界框标注。实验结果证明了基准数据增强设置对使用YOLO26检测腺病毒的影响，并指出了最有效的数据增强设置。
英文摘要
This study systematically benchmarks different data augmentation setups across the baseline YOLO26 model size variants to determine the most effective setup for adenovirus detection in TEM images. The benchmarked setups include NAS, GAS, GMAS, and DAS, all evaluated under identical training conditions. A modified YOLO26 model leveraging P2, expanded STAL, increased topk, and MuSGD was also tested across the same benchmarked setups. The adenovirus dataset, selected from the published TEM virus dataset, was re-annotated by leveraging adenovirus particle positions to generate YOLO-compatible bounding box annotations. The modifications produced their largest performance gains under GAS and GMAS, with modified YOLO26x trained using GAS achieving a mAP@50 of 0.80, Precision of 0.81 and a Recall of 0.80
