Generating synthetic data and training muzzle flash detection systems using GANs

Lih-Wei Chia; Mehul Motani

doi:10.1117/12.2668379

13 June 2023 Generating synthetic data and training muzzle flash detection systems using GANs

Lih-Wei Chia, Mehul Motani

Proceedings Volume 12521, Automatic Target Recognition XXXIII; 125210V (2023) https://doi.org/10.1117/12.2668379
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Researching gun muzzle flash detection can be costly and time-consuming, as data collection requires specialized equipment to be set up at various ranges and angles. This process is further complicated by the need to hire licensed weapon handlers for each weapon class, and by the scarcity of shooting ranges. To address this, we propose a novel approach that uses Generative Adversarial Networks (GANs) to speed up the research process. Specifically, we train a deep convolutional GAN (DCGAN) to generate synthetic muzzle flash waveforms, which can then be used to augment limited training data for deep-learning classifier models. We evaluate the performance of the DCGAN using a lightweight deep-learning model based on ResNet and explore the possibility of re-purposing the trained discriminator as a classifier.

Conference Presentation

Citation Download Citation

Lih-Wei Chia and Mehul Motani "Generating synthetic data and training muzzle flash detection systems using GANs", Proc. SPIE 12521, Automatic Target Recognition XXXIII, 125210V (13 June 2023); https://doi.org/10.1117/12.2668379

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