Object detection and recognition method based on binocular

Kaiyuan Zhu; Xiaobin Xu; Xining An; Benchao Wu; Xiaoyu Xu

doi:10.1117/12.2549966

20 December 2019 Object detection and recognition method based on binocular

Kaiyuan Zhu, Xiaobin Xu, Xining An, Benchao Wu, Xiaoyu Xu

Proceedings Volume 11209, Eleventh International Conference on Information Optics and Photonics (CIOP 2019); 112094X (2019) https://doi.org/10.1117/12.2549966
Event: Eleventh International Conference on Information Optics and Photonics (CIOP 2019), 2019, Xi'an, China

Abstract

This paper proposes a method based on 3D version to recognize free-form objects in the complicated environment, which could be used in field of robot picking. The point cloud data is generated by binocular camera system. The depth map is employed to recover 3D point cloud of the scenario with the calibration method from the binocular camera. Segmentation algorithm is used to detect the object. Recognition is performed by using software libraries integrated with custom-developed segmentation algorithm and model database created by the same binocular camera system. Experiments are designed to verify the performance of the method by randomly placing different types of experimental objects in manipulator workspace. The preliminary results demonstrate the excellent ability of the system to perform object recognition and picking.

Citation Download Citation

Kaiyuan Zhu, Xiaobin Xu, Xining An, Benchao Wu, and Xiaoyu Xu "Object detection and recognition method based on binocular", Proc. SPIE 11209, Eleventh International Conference on Information Optics and Photonics (CIOP 2019), 112094X (20 December 2019); https://doi.org/10.1117/12.2549966

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available