发表机构
Cornell University（康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对复杂果园中小目标检测与实例分割难题，对YOLOv8、YOLOv11、YOLOv26开展跨代基准测试，发现YOLOv11s-960精度最优、YOLOv26s-960效率与精度均衡，为农业机器人感知提供实用基准。
AI 中文摘要
在果园环境中，小目标检测与实例分割仍具挑战性，原因包括果叶颜色相近、遮挡以及果实精细结构的像素表征有限。本研究针对用于果园机器人感知的幼苹果、花萼和果柄结构，开展Ultralytics YOLOv8、YOLOv11和YOLOv26的跨代基准测试。在常规640×640及针对小目标的960×960训练配置下，评估了5种模型规模（n、s、m、l、x），共完成30组实验。模型容量提升并未持续提高精度，其中YOLOv11s-960实现了最高的掩码mAP@50:95（0.402）和框mAP@50:95（0.426），YOLOv26s-960仅用10.37M参数和34.1 GFLOPs就达到了0.397和0.425的可比数值。果柄仍是最具挑战性的类别。总体而言，兼具紧凑到中等规模的YOLO模型与小目标聚焦训练实现了良好的精度效率权衡，为细粒度农业机器人及果园感知建立了实用基准。
英文摘要
This study presents an architectural and experimental cross-generation analysis of Ultralytics YOLO27 (YOLOv27), YOLO26 (YOLOv26), YOLO11 (YOLOv11), and YOLOv8 for fine-grained robotic perception in complex orchard environments. Fine-grained detection and instance segmentation of early-stage fruit anatomy remain challenging in complex orchard environments because of limited pixel footprints, green-on-green similarity, occlusion, and substantial scale variation. Because YOLO27 has been announced but its public implementation and trainable segmentation models are not yet available, the present study provides an architectural analysis of YOLO27, while controlled experiments benchmark YOLOv8, YOLO11, and YOLO26; YOLO27 experiments will be incorporated following public model availability. Five model scales-nano (n), small (s), medium (m), large (l), and extra-large (x)-were evaluated for fruitlet, calyx, and peduncle detection and segmentation using conventional 640 x 640 and small-object-focused 960 x 960 configurations, yielding 30 experiments. YOLO11s-960 achieved the highest observed mask mAP@50:95(0.402) and box mAP@50:95(0.426), whereas YOLO26s-960 achieved comparable values of 0.397 and 0.425 with only 10.37~M parameters and 34.1~GFLOPs. Peduncle remained the most challenging class, and increasing model capacity did not consistently improve accuracy. Overall, compact-to-moderate YOLO models combined with small-object-focused training provided favorable accuracy-efficiency trade-offs, establishing a reproducible benchmark for fine-grained agricultural robotic perception. Code, trained models, and experimental configurations are publicly available, and will be updated through our Github Link: https://github.com/rnjnspkt/Optimizing-and-Comparing-Ultralytics-YOLOv26-YOLOv11-and-YOLOv8-for-Small-Object-Detection-and-Seg
