Training CNNs from Synthetic Data for Part Handling in Industrial Environments

As Convolutional Neural Network based models become reliable and efficient, two questions arise in relation to their applications for industrial purposes. The usefulness of these models in industrial environments and their implementation in these settings. This paper describes the autonomous generation of Region based CNN models trained on images from rendered CAD models and examines their applicability and performance for part handling application. The development of the automated synthetic data generation is detailed and two CNN models are trained with the aim to detect a car component and differentiate it against another similar looking part. The performance of these models is tested on real images and it was found that the proposed approach can be easily adopted for detecting a range of parts in arbitrary backgrounds. Moreover, the use of syntheic images for training CNNs automates the process of generating a detector.