In the manufacturing industry, computer vision systems based on artificial intelligence (AI) are widely used to reduce costs and increase production. Training these AI models requires a large amount of training data that is costly to acquire and annotate, especially in high-variance, low-volume manufacturing environments. A popular approach to reduce the need for real data is the use of synthetic data that is generated by leveraging computer-aided design (CAD) models available in the industry. However, in the agricultural industry these models are not readily available, increasing the difficulty in leveraging synthetic data. In this paper, we present different techniques for substituting CAD files to create synthetic datasets. We measure their relative performance when used to train an AI object detection model to separate stones and potatoes in a bin picking environment. We demonstrate that using highly representative 3D models acquired by scanning or using image-to-3D approaches can be used to generate synthetic data for training object detection models. Finetuning on a small real dataset can significantly improve the performance of the models and even get similar performance when less representative models are used.

翻译：在制造业中，基于人工智能（AI）的计算机视觉系统被广泛用于降低成本和提高产量。训练这些AI模型需要大量训练数据，这些数据的获取和标注成本高昂，尤其是在高方差、低产量的制造环境中。减少对真实数据需求的一种流行方法是利用工业中可用的计算机辅助设计（CAD）模型生成合成数据。然而，在农业领域，这些模型并不容易获得，这增加了利用合成数据的难度。本文提出了替代CAD文件以创建合成数据集的不同技术。我们测量了它们在用于训练AI目标检测模型以在分拣环境中区分石头和土豆时的相对性能。我们证明，通过扫描或使用图像到三维方法获取的高代表性三维模型可用于生成训练目标检测模型的合成数据。在小型真实数据集上进行微调可以显著提高模型的性能，甚至在使用代表性较低的模型时也能获得类似的性能。