Mr. Vicente Bolea Profile

Vicente Bolea

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 7 June 2024 Presentation + Paper

NRTK: an open source natural robustness toolkit for the evaluation of computer vision models

Brian Hu, Brandon RichardWebster, Paul Tunison, Emily Veenhuis, Bharadwaj Ravichandran, Alexander Lynch, Stephen Crowell, Alessandro Genova, Vicente Bolea, Sebastien Jourdain, Austin Whitesell

Proceedings Volume 13054, 130540C (2024) https://doi.org/10.1117/12.3012769

KEYWORDS: Data modeling, Visual process modeling, Performance modeling, Sensors, Computer vision technology, Visualization, Image sensors, Image visualization, Object detection

Read Abstract +

Recent progress in computer vision has been fueled by advances in artificial intelligence (AI) using techniques such as deep learning. However, computer vision models are often sensitive to distribution shifts, such as those naturally occurring in real operational data, which can degrade performance and cause unforeseen failure modes. As such, it is critical to perform comprehensive test and evaluation of these models across a wide range of natural effects in order to assure their downstream use and deployment. However, it is often difficult, impractical or simply impossible to collect and annotate datasets incorporating the expected range of conditions in operational domains. Here, we introduce the open source natural robustness toolkit (NRTK), a platform for evaluating the natural robustness of computer vision models through realistic augmentations of datasets. The NRTK package provides a set of validated scene- and sensor-specific perturbations using physics-based modeling of the entire image formation process, covering pre-sensor, at-sensor, and post-sensor perturbations. We demonstrate its modular and flexible nature through the example use cases of evaluating object detection models on satellite and unmanned aerial vehicle (UAV) imagery, where critical sensor parameters such as focal length and aperture diameter can be varied to assess model performance. We also show how the NRTK package can be paired with visualization tools to support the interactive exploration of datasets and their perturbations via NRTK. Our results suggest that natural robustness is a critical dimension for evaluation towards the verification and validation of computer vision models, ensuring their trusted use and deployment. Our code is publicly available at: https://github.com/Kitware/nrtk.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

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

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years