Borstell, Hagen and Nonnen, Jan (2019) Simulation of image data to support the training of convolutional neural networks for objects recognition. ADVANCED LOGISTIC SYSTEMS: THEORY AND PRACTICE, 13 (1). pp. 37-45. ISSN 1789-2198
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Abstract
The recognition of logistics objects is an essential prerequisite for the optimization of operational logistics processes and can be performed among others via image-based methods. However, the lack of available data for training domain-specific recognition models remains a practical problem. For this reason, we present an approach to solving this problem. The core principle of our approach is the automated generation of image data from 3D models, in which the appearance of the objects varies through variations of different parameters. The first results are promising: Without any real image data, we have created a neural network for recognition of real objects with a recall quality of 86%.
| Item Type: | Article |
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| Uncontrolled Keywords: | Logistics; Image Processing; Deep Learning; Simulation |
| Subjects: | H Social Sciences / társadalomtudományok > HF Commerce / kereskedelem > HF5001-6182 Business management / üzleti menedzsment Q Science / természettudomány > QA Mathematics / matematika > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány |
| Depositing User: | Beáta Bavalicsné Kerekes |
| Date Deposited: | 25 May 2023 12:53 |
| Last Modified: | 25 May 2023 12:53 |
| URI: | http://real.mtak.hu/id/eprint/166133 |
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