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.
Performing reliable target recognition in infrared imagery is a challenging problem due to the variation of the signatures of the targets caused by changes in the environment, the viewpoint or the state of the targets. Due to their state-of-the-art performance on several computer vision problems, Convolutional Neural Networks (CNNs) are particularly appealing in this context. However, CNNs may provide wrong classification results with high confidence. Robustness to disturbed inputs can be mitigated through the implementation of specific training strategies to improve classification performances. But they would generally require retraining or fine-tuning the CNN to face new forms of disturbed inputs. Besides, such strategies do not necessarily tackle novelty detection without training an auxiliary classifier. In this paper we propose two solutions to give the ability of a trained CNN to deal with both adversarial examples and novelty detection during inference. The first approach is based on one-class support vector machines (SVM) and the second one relies on the Local Outlier Factor (LOF) algorithm for example detection. We benchmark our contributions on SENSIAC database for a pre-trained network and evaluate how they may help mitigate false classifications on outliers and adversarial inputs.