Presentation + Paper
20 September 2020 Faster-RCNN with a compact CNN backbone for target detection in infrared images
Author Affiliations +
Abstract
The main goal of object detection is to localize objects in a given image and assign to each object fits corresponding class label. Performing effective approaches in infrared images is a challenging problem due to the variation of the target signature caused by changes in the environment, viewpoint variation or the state of the target. Convolutional Neural Networks (CNN) models already lead to accurate performances on traditional computer vision problems, and they have also show their capabilities to more specific applications like radar, sonar or infrared imaging. For target detection, two main approaches can be used: two-stage detector or one-stage detector. In this contribution we investigate the two-stage Faster-RCNN approach and propose to use a compact CNN model as backbone in order to speed-up the computational time without damaging the detection performance. The proposed model is evaluated on the dataset SENSIAC, made of 16 bits gray-value image sequences, and compared to Faster-RCNN with VGG19 as backbone and the one-stage model SSD.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Alexandre Baussard, Antoine d'Acremont, Guillaume Quin, and Ronan Fablet "Faster-RCNN with a compact CNN backbone for target detection in infrared images", Proc. SPIE 11543, Artificial Intelligence and Machine Learning in Defense Applications II, 1154307 (20 September 2020); https://doi.org/10.1117/12.2575756
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Infrared imaging

Infrared radiation

Sensors

Target detection

Infrared detectors

Automatic target recognition

Image segmentation

Back to Top