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15 March 2019 Generation of synthetic training data for object detection in piles
Elvijs Buls, Roberts Kadikis, Ričards Cacurs, Jānis Ārents
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Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 110411Z (2019) https://doi.org/10.1117/12.2523203
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany
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.
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Elvijs Buls, Roberts Kadikis, Ričards Cacurs, and Jānis Ārents "Generation of synthetic training data for object detection in piles", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110411Z (15 March 2019); https://doi.org/10.1117/12.2523203
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Cited by 2 scholarly publications.
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