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JACIII Vol.25 No.2 pp. 204-212
doi: 10.20965/jaciii.2021.p0204
(2021)

Paper:

An Improved Algorithm for Detection and Pose Estimation of Texture-Less Objects

Jian Peng*,** and Ya Su*,**

*School of Automation, China University of Geosciences
388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China

**Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
388 Lumo Road, Hongshan, Wuhan, Hubei 430074, China

Received:
September 29, 2020
Accepted:
December 22, 2020
Published:
March 20, 2021
Keywords:
computer vision, object detection and pose estimation, LineMOD algorithm
Abstract
An Improved Algorithm for Detection and Pose Estimation of Texture-Less Objects

We proposed the multi-scale template training method and a method called coarse positioning of objects

This paper introduces an improved algorithm for texture-less object detection and pose estimation in industrial scenes. In the template training stage, a multi-scale template training method is proposed to improve the sensitivity of LineMOD to template depth. When this method performs template matching, the test image is first divided into several regions, and then training templates with similar depth are selected according to the depth of each test image region. In this way, without traversing all the templates, the depth of the template used by the algorithm during template matching is kept close to the depth of the target object, which improves the speed of the algorithm while ensuring that the accuracy of recognition will not decrease. In addition, this paper also proposes a method called coarse positioning of objects. The method avoids a lot of useless matching operations, and further improves the speed of the algorithm. The experimental results show that the improved LineMOD algorithm in this paper can effectively solve the algorithm’s template depth sensitivity problem.

Cite this article as:
Jian Peng and Ya Su, “An Improved Algorithm for Detection and Pose Estimation of Texture-Less Objects,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.2, pp. 204-212, 2021.
Data files:
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Last updated on Aug. 03, 2021