Deep learning-based depth map defect removal for industrial applications

V. Voronin; N. Gapon; M. Zhdanova; O. Tokareva; I. Khamidullin; E. Semenishchev

doi:10.1117/12.2690886

27 November 2023 Deep learning-based depth map defect removal for industrial applications

V. Voronin, N. Gapon, M. Zhdanova, O. Tokareva, I. Khamidullin, E. Semenishchev

Proceedings Volume 12769, Optical Metrology and Inspection for Industrial Applications X; 127691Q (2023) https://doi.org/10.1117/12.2690886
Event: SPIE/COS Photonics Asia, 2023, Beijing, China

Conference Poster

Abstract

A system for determining the distance from the robot to the scene is useful for object tracking, and 3-D reconstructions may be desired for many manufacturing and robotic tasks. While the robot is processing materials, such as welding parts, milling, drilling, etc., fragments of materials fall on the camera installed on the robot, introducing unnecessary information when building a depth map, as well as the emergence of new lost areas, which leads to incorrect determination of the size of objects. There is a problem comprising a decrease in the accuracy of planning the movement trajectory caused by wrong sections on the depth map because of erroneous distance determination to objects. We present an approach combining defect detection and depth reconstruction algorithms. The first step for image defect detection is based on a convolutional auto-encoder (U-Net). The second step is a depth map reconstruction using a spatial reconstruction based on a geometric model with contour and texture analysis. We apply contour restoration and texture synthesis for image reconstruction. A method is proposed for restoring the boundaries of objects in an image based on constructing a composite curve by cubic splines. Our technique outperforms the state-of-the-art methods quantitatively in reconstruction accuracy on the RGB-D benchmark for evaluating manufacturing vision systems.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

V. Voronin, N. Gapon, M. Zhdanova, O. Tokareva, I. Khamidullin, and E. Semenishchev "Deep learning-based depth map defect removal for industrial applications", Proc. SPIE 12769, Optical Metrology and Inspection for Industrial Applications X, 127691Q (27 November 2023); https://doi.org/10.1117/12.2690886

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