A Comparative Study of 3D Model Acquisition Methods for Synthetic Data Generation of Agricultural Products
This addresses the challenge of costly data acquisition for AI in low-volume agricultural manufacturing, but it is incremental as it adapts existing synthetic data techniques to a specific domain.
The paper tackled the problem of generating synthetic data for training AI object detection models in agriculture, where CAD models are scarce, by comparing 3D model acquisition methods and showing that highly representative models combined with finetuning on small real datasets can achieve similar performance to less representative ones.
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.