使用LLM生成数据集进行零样本自动标注与实例分割：消除深度学习模型开发中的实地成像与手动标注
Zero-Shot Automatic Annotation and Instance Segmentation using LLM-Generated Datasets: Eliminating Field Imaging and Manual Annotation for Deep Learning Model Development
AI总结：
本文提出一种零样本方法，利用LLM合成果园图像，结合SAM和YOLO11自动标注，训练苹果实例分割模型，在真实图像上验证有效，消除了实地成像和手动标注的需求。
摘要：
目前，针对各种应用（如农业）的基于深度学习的实例分割，主要采用劳动密集型流程，包括使用精密传感器进行广泛的实地数据收集，随后进行仔细的图像手动标注，这给研究人员和组织带来了重大的后勤和财务挑战。该流程还减缓了模型开发和训练过程。在本研究中，我们提出了一种用于商业果园中苹果的基于深度学习的实例分割的新方法，该方法消除了劳动密集型的实地数据收集和手动标注的需求。利用大型语言模型（LLM），我们合成生成了果园图像，并使用集成了YOLO11基础模型的Segment Anything Model（SAM）自动进行标注。该方法显著减少了对物理传感器和手动数据处理的依赖，代表了“农业人工智能”的重大进步。合成的自动标注数据集用于训练YOLO11模型进行苹果实例分割，并在真实果园图像上进行了验证。结果表明，自动生成的标注实现了Dice系数为0.9513和IoU为0.9303，验证了掩膜标注的准确性和重叠度。所有仅在这些合成数据集上训练的YOLO11配置，均能准确识别和描绘苹果，突显了该方法的有效性。具体而言，YOLO11m-seg配置在商业果园采集的测试图像上实现了掩膜精度0.902和掩膜mAP@50为0.833。此外，YOLO11l-seg配置在40张LLM生成图像的验证中优于其他模型，实现了最高的掩膜精度和mAP@50指标。
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
