Experimental test platform.

Oak seeds are highly susceptible to pest infestations due to their elevated starch content, which significantly impairs germination and subsequent growth. To address this challenge, we developed a high-resolution imaging system and proposed an improved YOLO-based model named Oak-YOLO for efficient and accurate defect detection in oak seeds. The proposed model enhances the YOLOv8 architecture by incorporating EfficientViT as the backbone to improve global feature extraction, and integrates a Ghost-DynamicConv detection head to enhance the representation of small and irregular defects such as insect holes and cracks. Additionally, the WIoUv3 loss function is introduced to optimize bounding box regression for complex target shapes and overlapping instances.Extensive experiments were conducted on both single-object and multi-object datasets. Oak-YOLO achieved a mAP50 of 94.5%, an F1-score of 95.3%, and a precision of 94.% on the oak-intensive dataset, with an inference speed of 132.2 FPS. Cross-device validation using mobile-captured images further demonstrated the model’s robustness, achieving mAP50 scores of 94.7% and 93.8% on different smartphone test sets. Comparative evaluations show that Oak-YOLO outperforms existing YOLO models, including YOLOv9 to YOLOv12, by delivering a favorable trade-off between detection accuracy and computational efficiency. These results highlight the potential of Oak-YOLO as a practical solution for real-time seed quality inspection in forestry applications.

橡籽（Oak seeds）因淀粉含量较高，极易遭受虫害侵扰，这会严重损害其发芽能力与后续生长发育。为应对这一挑战，我们搭建了一套高分辨率成像系统，并提出了一种基于改进YOLO的模型Oak-YOLO，用于橡籽的高效精准缺陷检测。所提模型对YOLOv8架构进行了优化：以EfficientViT作为主干网络以提升全局特征提取能力，并集成Ghost-DynamicConv检测头，以增强对虫洞、裂纹等小型不规则缺陷的特征表征效果。此外，我们引入WIoUv3损失函数，以针对复杂目标形状与重叠目标实例优化边界框回归任务。我们在单目标与多目标数据集上开展了大量实验。在橡籽密集型数据集上，Oak-YOLO的mAP50达到94.5%，F1分数为95.3%，精确率为94.0%，推理速度达132.2 FPS（Frames Per Second）。通过移动设备采集的图像开展跨设备验证，进一步证实了该模型的鲁棒性：在两款不同智能手机的测试集上，其mAP50分别达到94.7%与93.8%。对比评估结果显示，Oak-YOLO在检测精度与计算效率间实现了优异的平衡，其性能优于现有YOLO系列模型（涵盖YOLOv9至YOLOv12）。上述结果表明，Oak-YOLO有望成为林业场景中实时种子质量检测的实用解决方案。