Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/35821
Title: Real-time Detection of 2D Tool Landmarks with Synthetic Training Data
Authors: VANHERLE, Bram 
PUT, Jeroen 
MICHIELS, Nick 
VAN REETH, Frank 
Issue Date: 2021
Publisher: Scitepress
Source: Galambos, Péter; Kayacan, Erdal (Ed.). Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems, Scitepress, p. 40 -47 (Art N° 4)
Abstract: In this paper a deep learning architecture is presented that can, in real time, detect the 2D locations of certain landmarks of physical tools, such as a hammer or screwdriver. To avoid the labor of manual labeling, the network is trained on synthetically generated data. Training computer vision models on computer generated images, while still achieving good accuracy on real images, is a challenge due to the difference in domain. The proposed method uses an advanced rendering method in combination with transfer learning and an intermediate supervision architecture to address this problem. It is shown that the model presented in this paper, named Intermediate Heatmap Model (IHM), generalizes to real images when trained on synthetic data. To avoid the need for an exact textured 3D model of the tool in question, it is shown that the model will generalize to an unseen tool when trained on a set of different 3D models of the same type of tool. IHM is compared to two existing approaches to keypoint detection and it is shown that it outperforms those at detecting tool landmarks, trained on synthetic data.
Keywords: Object Keypoint Detection;Deep Learning;Synthetic Data Generation
Document URI: http://hdl.handle.net/1942/35821
ISBN: 9789897585371
DOI: 10.5220/0010689900003061
Category: C1
Type: Proceedings Paper
Appears in Collections:Research publications

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