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Deep Learning Based Object Recognition Using Physically-Realistic Synthetic Depth Scenes

Department of Robotics, Nazarbayev University, 53 Kabanbay batyr Ave., Astana Z05H0P9, Kazakhstan
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Mach. Learn. Knowl. Extr. 2019, 1(3), 883-903; https://doi.org/10.3390/make1030051
Received: 29 March 2019 / Revised: 3 August 2019 / Accepted: 4 August 2019 / Published: 6 August 2019
(This article belongs to the Section Learning)
Recognizing objects and estimating their poses have a wide range of application in robotics. For instance, to grasp objects, robots need the position and orientation of objects in 3D. The task becomes challenging in a cluttered environment with different types of objects. A popular approach to tackle this problem is to utilize a deep neural network for object recognition. However, deep learning-based object detection in cluttered environments requires a substantial amount of data. Collection of these data requires time and extensive human labor for manual labeling. In this study, our objective was the development and validation of a deep object recognition framework using a synthetic depth image dataset. We synthetically generated a depth image dataset of 22 objects randomly placed in a 0.5 m × 0.5 m × 0.1 m box, and automatically labeled all objects with an occlusion rate below 70%. Faster Region Convolutional Neural Network (R-CNN) architecture was adopted for training using a dataset of 800,000 synthetic depth images, and its performance was tested on a real-world depth image dataset consisting of 2000 samples. Deep object recognizer has 40.96% detection accuracy on the real depth images and 93.5% on the synthetic depth images. Training the deep learning model with noise-added synthetic images improves the recognition accuracy for real images to 46.3%. The object detection framework can be trained on synthetically generated depth data, and then employed for object recognition on the real depth data in a cluttered environment. Synthetic depth data-based deep object detection has the potential to substantially decrease the time and human effort required for the extensive data collection and labeling. View Full-Text
Keywords: machine learning; convolutional neural networks; deep learning; object recognition; synthetic data generation; big data; physics engine machine learning; convolutional neural networks; deep learning; object recognition; synthetic data generation; big data; physics engine
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Baimukashev, D.; Zhilisbayev, A.; Kuzdeuov, A.; Oleinikov, A.; Fadeyev, D.; Makhataeva, Z.; Varol, H.A. Deep Learning Based Object Recognition Using Physically-Realistic Synthetic Depth Scenes. Mach. Learn. Knowl. Extr. 2019, 1, 883-903.

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