Influence of different image acquisition sources on the performances of a CNN-based object detector

Nicolas Hueber; Alexander Pichler

doi:10.1117/12.2686400

17 October 2023 Influence of different image acquisition sources on the performances of a CNN-based object detector

Nicolas Hueber, Alexander Pichler

Proceedings Volume 12742, Artificial Intelligence for Security and Defence Applications; 127420G (2023) https://doi.org/10.1117/12.2686400
Event: SPIE Security + Defence, 2023, Amsterdam, Netherlands

Abstract

Mastering the content quality, diversity and context representativeness of a database is a key step to efficiently trained deep learning models. This project aims at controlling the relevant hyper-parameters of training datasets to be able to guarantee a mission performance and to contribute to the explainability of the model behavior. In this presentation, we show an approach to design DRI (Detection, Recognition and Identification) algorithms of military vehicles with different acquisition sources. Starting from a definition of a mission-agnostic image database, this study is focused on controlled image acquisition sources which automates the collection of few but relevant object signatures and their metadata e.g. bounding box, segmentation mask, view angles, object orientations, lighting conditions... By putting the accent on the acquisition of a reduced amount of images coupled with data augmentation technics, it is foreseen to demonstrate a dataset creation method that is fast, efficient, controlled and easily adaptable to new mission scenarios and contexts. This study compares three different sources: an optical acquisition bench of scaled vehicle model, the 3D scanning of scaled models and 3D graphic model of vehicles. The challenge is to make predictions on real situations with a neural network model only trained with the generated images. First results obtained with the datasets extracted from the 3D environment with graphic models and with scanned scaled ones are not yet reaching the previous performance levels obtained with 2D acquisition bench. Further investigations are needed to understand the influence of the numerous hyper-parameters introduced by the 3D environment simulation.

Conference Presentation

Citation Download Citation

Nicolas Hueber and Alexander Pichler "Influence of different image acquisition sources on the performances of a CNN-based object detector", Proc. SPIE 12742, Artificial Intelligence for Security and Defence Applications, 127420G (17 October 2023); https://doi.org/10.1117/12.2686400

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
PRESENTATION

WATCH
PRESENTATION SAVE TO MY LIBRARY

GET CITATION

KEYWORDS

Image acquisition

Visual process modeling

3D modeling

Data modeling

Education and training

Object detection

Defense and security

Show All Keywords

Keywords/Phrases

Search In:

Publication Years