Faster-RCNN with a compact CNN backbone for target detection in infrared images

Alexandre Baussard; Antoine d'Acremont; Guillaume Quin; Ronan Fablet

doi:10.1117/12.2575756

20 September 2020 Faster-RCNN with a compact CNN backbone for target detection in infrared images

Alexandre Baussard, Antoine d'Acremont, Guillaume Quin, Ronan Fablet

Proceedings Volume 11543, Artificial Intelligence and Machine Learning in Defense Applications II; 1154307 (2020) https://doi.org/10.1117/12.2575756
Event: SPIE Security + Defence, 2020, Online Only

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

Citation Download Citation

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

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