用大模型生成苹果图像并自动标注，免去实地拍摄和人工标注。
Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development
- 用大模型合成果园图像，再用SAM+YOLO11自动标注。
- 自动生成标注的掩码Dice达0.9513，IoU达0.9303。
- 适合农业AI研究者快速训练实例分割模型。
当前基于深度学习的实例分割在农业等场景中，依赖大量实地采集数据与人工标注，成本高、周期长。本文提出一种新方法，利用大语言模型（LLM）合成商业果园苹果图像，并通过集成YOLO11与分割一切模型（SAM）实现自动标注。该合成数据集用于训练YOLO11模型进行苹果实例分割，在真实果园图像上验证。结果表明，自动生成的标注具有0.9513的Dice系数和0.9303的IoU，精度可靠。所有配置均准确识别苹果，其中YOLO11m-seg在真实测试图像上取得0.902的掩码精度和0.833的mAP@50；YOLO11l-seg在40张合成图像上表现最优，优于其他模型。该方法显著减少对物理传感器与人工标注的依赖，推动农业人工智能发展。
原文摘要 · Abstract (English)
Currently, deep learning-based instance segmentation for various applications (e.g., Agriculture) is predominantly performed using a labor-intensive process involving extensive field data collection using sophisticated sensors, followed by careful manual annotation of images, presenting significant logistical and financial challenges to researchers and organizations. The process also slows down the model development and training process. In this study, we presented a novel method for deep learning-based instance segmentation of apples in commercial orchards that eliminates the need for labor-intensive field data collection and manual annotation. Utilizing a Large Language Model (LLM), we synthetically generated orchard images and automatically annotated them using the Segment Anything Model (SAM) integrated with a YOLO11 base model. This method significantly reduces reliance on physical sensors and manual data processing, presenting a major advancement in "Agricultural AI". The synthetic, auto-annotated dataset was used to train the YOLO11 model for Apple instance segmentation, which was then validated on real orchard images. The results showed that the automatically generated annotations achieved a Dice Coefficient of 0.9513 and an IoU of 0.9303, validating the accuracy and overlap of the mask annotations. All YOLO11 configurations, trained solely on these synthetic datasets with automated annotations, accurately recognized and delineated apples, highlighting the method's efficacy. Specifically, the YOLO11m-seg configuration achieved a mask precision of 0.902 and a mask mAP@50 of 0.833 on test images collected from a commercial orchard. Additionally, the YOLO11l-seg configuration outperformed other models in validation on 40 LLM-generated images, achieving the highest mask precision and mAP@50 metrics. Keywords: YOLO, SAM, SAMv2, YOLO11, YOLOv11, Segment Anything, YOLO-SAM
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。