Road scene object detection using pre-trained RGB neural networks on linear Stokes images

Khalid Omer; Russell Chipman; Meredith Kupinski

doi:10.1117/12.2557172

26 May 2020 Road scene object detection using pre-trained RGB neural networks on linear Stokes images

Khalid Omer, Russell Chipman, Meredith Kupinski

Proceedings Volume 11412, Polarization: Measurement, Analysis, and Remote Sensing XIV; 1141203 (2020) https://doi.org/10.1117/12.2557172
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

Neural networks trained on RGB and monochromatic images are tested on images augmented by polarimetry for recognition of road-based objects. The goal of this work is to understand the scene conditions for which object detection and recognition can be improved by linear Stokes measurements. Shadows, windows, low albedo, and other object features which reduce RGB image contrast also decrease neural network detection performance. This work demonstrates specific cases for which linear Stokes images increase image contrast and therefore increase object detection by a neural network. Linear Stokes videos for five difference scenes are collected at three times of day and two driving directions. Although limited in scope, this work demonstrates some enhancement to object detection by adding polarimetry to neural networks trained on RGB images.

Conference Presentation

Citation Download Citation

Khalid Omer, Russell Chipman, and Meredith Kupinski "Road scene object detection using pre-trained RGB neural networks on linear Stokes images", Proc. SPIE 11412, Polarization: Measurement, Analysis, and Remote Sensing XIV, 1141203 (26 May 2020); https://doi.org/10.1117/12.2557172

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