Count analysis tested on YOLO variants.

Fruit detection using the YOLO framework has fostered fruit yield prediction, fruit harvesting automation, fruit quality control, fruit supply chain efficiency, smart fruit farming, labor cost reduction, and consumer convenience. Nevertheless, the factors that affect fruit detectors, such as occlusion, illumination, target dense status, etc., including performance attributes like low accuracy, low speed, and high computation costs, still remain a significant challenge. To solve these problems, a collection of fruit images, termed the CFruit image dataset, was constructed, and the YOLOcF fruit detector was designed. The YOLOcF detector, which is an improved anchor-based YOLOv5, was compared to YOLOv5n, YOLOv7t, YOLOv8n, YOLOv9, YOLOv10n, and YOLOv11n of YOLO variants. The study findings indicate that the computation costs in terms of params and GFLOPs of YOLOcF are lower than those of other YOLO variants, except for YOLOv10n and YOLOv11n. The mAP of YOLOcF is 0.8%, 1.1%, 1.3%, 0.7%, and 0.8% more accurate than YOLOv5n, YOLOv7t, YOLOv8n, YOLOv10n, and YOLOv11n, respectively, but 1.4% less than YOLOv9t. The detection speed of YOLOcF, measured at 323 fps, exceeds that of other YOLO variants. YOLOcF is very robust and reliable compared to other YOLO variants for having the highest R2 of 0.422 value from count analysis. Thus, YOLOcF fruit detector is lightweight for easy mobile device deployment, faster for training, and robust for generalization.采用YOLO(You Only Look Once)框架开展水果目标检测，有力推动了水果产量预测、采收自动化、品质管控、供应链效能提升、智慧果园建设、人工成本压降以及消费者便利性改善。然而，影响水果目标检测器的各类因素（如遮挡、光照、目标密集状态等），以及其存在的精度不足、推理速度偏慢、计算成本高昂等性能短板，仍是亟待解决的重大挑战。为解决上述痛点，本研究构建了CFruit图像数据集，并设计了YOLOcF水果目标检测器。YOLOcF检测器属于基于锚框的改进型YOLOv5架构，本研究将其与多款YOLO变体（YOLOv5n、YOLOv7t、YOLOv8n、YOLOv9、YOLOv10n及YOLOv11n）开展了对比实验。研究结果表明，YOLOcF的参数量与GFLOPs(Giga Floating-point Operations Per Second)层面的计算成本，仅低于YOLOv10n和YOLOv11n，高于其余所有YOLO变体。YOLOcF的平均精度均值(mAP, mean Average Precision)分别较YOLOv5n、YOLOv7t、YOLOv8n、YOLOv10n及YOLOv11n提升0.8%、1.1%、1.3%、0.7%与0.8%，但较YOLOv9t低1.4%。YOLOcF的检测帧率可达323帧每秒(fps, frames per second)，优于所有对比的YOLO变体。在目标计数分析中，YOLOcF的决定系数(R², coefficient of determination)达0.422，为所有对比变体中的最高值，因此其鲁棒性与可靠性均优于其余YOLO变体。综上，YOLOcF水果目标检测器具备轻量化特性，便于移动端部署，训练效率更高且泛化能力出色。