用检测+分割联合方法，精准提取植物标本轮廓，提升分类准确率。
PlantSAM: An Object Detection-Driven Segmentation Pipeline for Herbarium Specimens
- 先用YOLOv10定位植物区域，再用SAM2精细分割，形成自动化流程。
- 分割精度达IoU 0.94、Dice 0.97，显著优于现有方法。
- 去除背景干扰后，分类准确率最高提升4.36%，适合植物图像分析研究者。
基于深度学习的植物标本图像分类受背景异质性影响，噪声和伪影可能误导模型并降低分类准确率。为应对这一挑战，本文提出PlantSAM，一种结合YOLOv10检测与Segment Anything Model（SAM2）分割的自动化分割流程。YOLOv10生成边界框提示以引导SAM2，提升分割精度。两模型均在植物标本图像上微调，并使用交并比（IoU）和Dice系数评估。PlantSAM实现当前最优分割性能，IoU达0.94，Dice系数达0.97。将分割后的图像用于分类模型，在五种植物性状测试中均取得一致性能提升，准确率最高提高4.36%，F1分数提升4.15%。结果表明，背景去除对植物标本图像分析至关重要，可使模型更专注地识别前景植物结构，显著提升分类准确性。
原文摘要 · Abstract (English)
Deep learning-based classification of herbarium images is hampered by background heterogeneity, which introduces noise and artifacts that can potentially mislead models and reduce classification accuracy. Addressing these background-related challenges is critical to improving model performance. We introduce PlantSAM, an automated segmentation pipeline that integrates YOLOv10 for plant region detection and the Segment Anything Model (SAM2) for segmentation. YOLOv10 generates bounding box prompts to guide SAM2, enhancing segmentation accuracy. Both models were fine-tuned on herbarium images and evaluated using Intersection over Union (IoU) and Dice coefficient metrics. PlantSAM achieved state-of-the-art segmentation performance, with an IoU of 0.94 and a Dice coefficient of 0.97. Incorporating segmented images into classification models led to consistent performance improvements across five tested botanical traits, with accuracy gains of up to 4.36% and F1-score improvements of 4.15%. Our findings highlight the importance of background removal in herbarium image analysis, as it significantly enhances classification accuracy by allowing models to focus more effectively on the foreground plant structures.
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