🤖 AI Summary
This study addresses the challenge of generating high-quality synthetic data for training AI vision systems in agricultural settings, where ready-made CAD models are typically unavailable—especially for highly variable, low-yield objects such as potatoes and stones. The work presents the first systematic comparison of non-CAD approaches, including 3D scanning and image-to-3D reconstruction, to create high-fidelity 3D models of agricultural objects for synthetic data generation. Experimental results demonstrate that object detectors trained on such synthetic data achieve strong performance, and after fine-tuning with only a small amount of real-world data, they approach the accuracy of models trained entirely on real data. These findings validate the feasibility and practical utility of high-fidelity, non-CAD-based 3D modeling for agricultural computer vision tasks.
📝 Abstract
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.