Despite the notable accomplishments of deep object detection models, a major challenge that persists is the requirement for extensive amounts of training data. The process of procuring such real-world data is a laborious undertaking, which has prompted researchers to explore new avenues of research, such as synthetic data generation techniques. This study presents a framework for the generation of synthetic datasets by fine-tuning pretrained stable diffusion models. The synthetic datasets are then manually annotated and employed for training various object detection models. These detectors are evaluated on a real-world test set of 331 images and compared against a baseline model that was trained on real-world images. The results of this study reveal that the object detection models trained on synthetic data perform similarly to the baseline model. In the context of apple detection in orchards, the average precision deviation with the baseline ranges from 0.09 to 0.12. This study illustrates the potential of synthetic data generation techniques as a viable alternative to the collection of extensive training data for the training of deep models.

翻译：尽管深度目标检测模型取得了显著成就，但仍面临一个主要挑战，即对大量训练数据的需求。获取此类真实世界数据的过程是一项繁重的工作，这促使研究人员探索新的研究方向，例如合成数据生成技术。本研究提出一个框架，通过微调预训练的稳定扩散模型来生成合成数据集。随后，对合成数据集进行手动标注，并用于训练各种目标检测模型。这些检测器在一个包含331张图像的真实世界测试集上进行评估，并与基于真实世界图像训练的基线模型进行比较。研究结果显示，基于合成数据训练的目标检测模型性能与基线模型相似。在果园苹果检测的场景中，与基线相比，平均精度偏差在0.09到0.12之间。本研究展示了合成数据生成技术作为训练深度模型所需大规模数据收集的可行替代方案的潜力。