Generation of synthetic training data for object detection in piles

Abstract

Current state-of-the-art object detectors are based on supervised deep learning approaches. These methods require a large amount of annotated training data, which hinders a wider use of these methods in industry. We propose a method for generating synthetic training data for the task of detecting which objects in a pile can be picked up by a robot arm. The method requires few input images, which are used to create annotated images of piles. After training a state-of-theart detector on the synthetic data, we test it on real images. The results show that the model trained in such a way is not a rival to the best object detectors trained on large datasets of real images, but it is good for the specific task of detecting pickable objects in the piles. The main advantage of the proposed training approach is that the existing models can be easily re-trained to work with piles of different objects by personnel who do not specialize in machine learning.