Deep Learning Based Object Recognition Using Physically-Realistic Synthetic Depth Scenes
AbstractRecognizing 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.
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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.
Baimukashev D, Zhilisbayev A, Kuzdeuov A, Oleinikov A, Fadeyev D, Makhataeva Z, Varol HA. Deep Learning Based Object Recognition Using Physically-Realistic Synthetic Depth Scenes. Machine Learning and Knowledge Extraction. 2019; 1(3):883-903.Chicago/Turabian Style
Baimukashev, Daulet; Zhilisbayev, Alikhan; Kuzdeuov, Askat; Oleinikov, Artemiy; Fadeyev, Denis; Makhataeva, Zhanat; Varol, Huseyin A. 2019. "Deep Learning Based Object Recognition Using Physically-Realistic Synthetic Depth Scenes." Mach. Learn. Knowl. Extr. 1, no. 3: 883-903.