用少量标注数据+大量无标签数据，提升农田杂草检测精度。
Semi-Supervised Weed Detection in Vegetable Fields: In-domain and Cross-domain Experiments
- 基于YOLOv8设计半监督检测框架WeedTeacher，利用无标签数据增强模型。
- 在同域场景下，mAP@50提升2.6%，mAP@50:95提升3.1%。
- 适合农业智能识别、标注成本高的场景，尤其关注跨域泛化问题。
精准除草中，鲁棒的杂草检测仍具挑战，不仅需要强大检测模型，还需大规模标注数据。但因识别植物种类需专业知识且耗时费力，获取充足标注数据极为困难。本研究提出一种半监督目标检测（SSOD）方法，以利用无标签数据提升杂草检测性能，并构建了基于YOLOv8的新型方法WeedTeacher。通过对比四种SSOD方法（DenseTeacher、EfficientTeacher、SmallTeacher及WeedTeacher）与全监督基线，在同域与跨域两种场景下进行实验。实验使用新构建的数据集，包含19,931张田间图像，来自两个不同域：8,435张标注图像（基础域，2021–2023年手持设备采集），11,496张未标注图像（新域，2024年地面移动平台采集）。同域实验中，仅使用10%标注数据训练，剩余90%测试，WeedTeacher在所有方法中表现最优，相比其监督基线（YOLOv8l）分别提升2.6% mAP@50和3.1% mAP@50:95。跨域实验中，尽管引入了新域无标签数据，所有方法均未显著优于监督基线。表明跨域数据利用仍是实现鲁棒杂草检测的关键挑战。
原文摘要 · Abstract (English)
Robust weed detection remains a challenging task in precision weeding, requiring not only potent weed detection models but also large-scale, labeled data. However, the labeled data adequate for model training is practically difficult to come by due to the time-consuming, labor-intensive process that requires specialized expertise to recognize plant species. This study introduces semi-supervised object detection (SSOD) methods for leveraging unlabeled data for enhanced weed detection and proposes a new YOLOv8-based SSOD method, i.e., WeedTeacher. An experimental comparison of four SSOD methods, including three existing frameworks (i.e., DenseTeacher, EfficientTeacher, and SmallTeacher) and WeedTeacher, alongside fully supervised baselines, was conducted for weed detection in both in-domain and cross-domain contexts. A new, diverse weed dataset was created as the testbed, comprising a total of 19,931 field images from two differing domains, including 8,435 labeled (basic-domain) images acquired by handholding devices from 2021 to 2023 and 11,496 unlabeled (new-domain) images acquired by a ground-based mobile platform in 2024. The in-domain experiment with models trained using 10% of the labeled, basic-domain images and tested on the remaining 90% of the data, showed that the YOLOv8-basedWeedTeacher achieved the highest accuracy among all four SSOD methods, with an improvement of 2.6% mAP@50 and 3.1% mAP@50:95 over its supervised baseline (i.e., YOLOv8l). In the cross-domain experiment where the unlabeled new-domain data was incorporated, all four SSOD methods, however, resulted in no or limited improvements over their supervised counterparts. Research is needed to address the difficulty of cross-domain data utilization for robust weed detection.
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