改进YOLOv8检测稻穗开花小目标，提升田间识别精度与速度。
An Improved YOLOv8 Approach for Small Target Detection of Rice Spikelet Flowering in Field Environments
- 用BiFPN替代PANet增强多尺度特征融合，加入p2小目标检测头减少特征损失。
- 在自建数据集上实现[email protected]达65.9%，比基线提升3.10%。
- 适合智能育种、田间自动化监测场景，兼顾高精度与实时性。
准确识别水稻开花时间对杂交稻种子生产中的及时授粉至关重要，可提高授粉效率并保障高产。然而，受田间环境复杂及稻穗小、开花周期短等特性影响，自动精准识别仍具挑战。为此，本文提出一种基于改进YOLOv8的目标检测方法。首先，用双向特征金字塔网络（BiFPN）替换原模型的PANet结构，提升特征融合能力与多尺度特征利用效率；其次，为增强小目标检测性能，引入p2小目标检测头，通过更精细的特征映射降低小目标检测中的特征丢失。针对田间稻穗开花缺乏公开数据集的问题，采用高分辨率RGB相机并结合数据增强技术构建专用数据集，为模型训练与测试提供可靠支持。实验结果表明，改进后的YOLOv8s-p2模型在测试集上达到[email protected]为65.9%、精确率67.6%、召回率61.5%、F1分数64.41%，相较基线模型分别提升3.10%、8.40%、10.80%和9.79%。模型推理速度达69帧/秒，满足实际应用需求。整体而言，该方法兼具高精度与高速度，为杂交稻种子生产的自动化监测提供了有效解决方案。
原文摘要 · Abstract (English)
Accurately detecting rice flowering time is crucial for timely pollination in hybrid rice seed production. This not only enhances pollination efficiency but also ensures higher yields. However, due to the complexity of field environments and the characteristics of rice spikelets, such as their small size and short flowering period, automated and precise recognition remains challenging. To address this, this study proposes a rice spikelet flowering recognition method based on an improved YOLOv8 object detection model. First, a Bidirectional Feature Pyramid Network (BiFPN) replaces the original PANet structure to enhance feature fusion and improve multi-scale feature utilization. Second, to boost small object detection, a p2 small-object detection head is added, using finer feature mapping to reduce feature loss commonly seen in detecting small targets. Given the lack of publicly available datasets for rice spikelet flowering in field conditions, a high-resolution RGB camera and data augmentation techniques are used to construct a dedicated dataset, providing reliable support for model training and testing. Experimental results show that the improved YOLOv8s-p2 model achieves an [email protected] of 65.9%, precision of 67.6%, recall of 61.5%, and F1-score of 64.41%, representing improvements of 3.10%, 8.40%, 10.80%, and 9.79%, respectively, over the baseline YOLOv8. The model also runs at 69 f/s on the test set, meeting practical application requirements. Overall, the improved YOLOv8s-p2 offers high accuracy and speed, providing an effective solution for automated monitoring in hybrid rice seed production.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。