2026-06-08原文2 min readUpdated: 2026-06-08

Applying Deep Learning for cockpit segmentation in the context of mixed reality

This paper proposes using U-net and DeepLabV3+ convolutional neural networks to segment foreground and background in cockpit images for mixed reality, achieving around 90% accuracy on images from a CAT793F truck simulator.

SourcearXiv Computer VisionAuthor: Alexandre Leles Sousa, Pedro de Oliveira Nielson, Erick Oliveira Rodrigues, Rafael Francisco dos Santos, Giovani Bernardes Vitor

[2606.06520] Applying Deep Learning for cockpit segmentation in the context of mixed reality

[Submitted on 2 Jun 2026]

Title:Applying Deep Learning for cockpit segmentation in the context of mixed reality

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Abstract:Computer vision is an area that has been growing continuously. With the advance of technologies with a first-person view, new development opportunities have emerged inside the area. Mixed reality promotes virtual environments with objects from the physical world shown in real time. For that, it's necessary to be concerned with the immersion of the user in this simulated environment, increasingly seeking to bring it closer to a possible desired reality. This paper proposes the development of image processing in order to perform the segmentation of images to identify what is foreground and background in order to facilitate the union of virtual and real images. Thus, the present work obtain real images of the user using the off-highway truck simulator CAT793F, through a camera, to be able to perform the segmentation of such images with artificial intelligence this http URL convolutional neural network architectures "U-net" and "DeepLabV3+" are applied to perform image segmentation. As a result, metrics with around 90% accuracy were presented and and the best model was determined.

Comments: XXV Congresso Brasileiro de Automática - CBA 2024

Subjects:

Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)

Cite as: arXiv:2606.06520 [cs.CV]

(or arXiv:2606.06520v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.06520

arXiv-issued DOI via DataCite

Related DOI:

https://doi.org/10.20906/CBA2024/4844

DOI(s) linking to related resources

Submission history

From: Erick Rodrigues [view email] [v1] Tue, 2 Jun 2026 16:43:24 UTC (3,767 KB)

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