CNN-Based Target Recognition and Identification for Infrared Imaging in Defense Systems

D’acremont, Antoine; Fablet, Ronan; Baussard, Alexandre; Quin, Guillaume

doi:10.3390/s19092040

Cited by 37 publications

(23 citation statements)

References 39 publications

(53 reference statements)

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“…According to the scale of the CIFAR100 dataset, we randomly selected 1000 training samples (including 100 categories) from 50,000 training images using the Monte Carlo method. For the selected training images, the CNN_mean, CNN_max [45], ACGAN and CP-ACGAN methods were used for the training procedure. The classification accuracy effect of different methods on the CIFAR100 training dataset after training 1000 samples is shown in Figure 12.…”

Section: The Experimental Results and Analysismentioning

confidence: 99%

The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN

Chen

Tao

Wang

et al. 2019

Sensors

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To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing using auxiliary classifier generative adversarial networks (ACGAN) is proposed in this paper. Firstly, the real/fake discrimination of sensor samples in the network has been canceled at the output layer of the discriminative network and only the posterior probability estimation of the sample tag is outputted. Secondly, by regarding the real sensor samples as supervised data and the generative sensor samples as labeled fake data, we have reconstructed the loss function of the generator and discriminator by using the real/fake attributes of sensor samples and the cross-entropy loss function of the label. Thirdly, the pooling and caching method has been introduced into the discriminator to enable more effective extraction of the classification features. Finally, feature matching has been added to the discriminative network to ensure the diversity of the generative sensor samples. Experimental results have shown that the proposed algorithm (CP-ACGAN) achieves better classification accuracy on the MNIST dataset, CIFAR10 dataset and CIFAR100 dataset than other solutions. Moreover, when compared with the ACGAN and CNN classification algorithms, which have the same deep network structure as CP-ACGAN, the proposed method continues to achieve better classification effects and stability than other main existing sensor solutions.

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Section: The Experimental Results and Analysismentioning

confidence: 99%

The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN

Chen

Tao

Wang

et al. 2019

Sensors

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show abstract

“…The achieved result demonstrates that our method could already be used for several applications such as infrared security cameras. Based on the main concepts used in the development of the method presented in this paper, we plan to work further on the development of new methods for infrared object tracking in images with dynamic backgrounds and cluttered object space, focusing on such applications as autonomous driving or military applications (such as those described in [57,58]) based on the concepts and ideas which were successfully validated in this paper.…”

Section: Discussion and Final Remarksmentioning

confidence: 99%

Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

Zhu

Lou

et al. 2020

Symmetry

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Infrared image recognition technology can work day and night and has a long detection distance. However, the infrared objects have less prior information and external factors in the real-world environment easily interfere with them. Therefore, infrared object classification is a very challenging research area. Manifold learning can be used to improve the classification accuracy of infrared images in the manifold space. In this article, we propose a novel manifold learning algorithm for infrared object detection and classification. First, a manifold space is constructed with each pixel of the infrared object image as a dimension. Infrared images are represented as data points in this constructed manifold space. Next, we simulate the probability distribution information of infrared data points with the Gaussian distribution in the manifold space. Then, based on the Gaussian distribution information in the manifold space, the distribution characteristics of the data points of the infrared image in the low-dimensional space are derived. The proposed algorithm uses the Kullback-Leibler (KL) divergence to minimize the loss function between two symmetrical distributions, and finally completes the classification in the low-dimensional manifold space. The efficiency of the algorithm is validated on two public infrared image data sets. The experiments show that the proposed method has a 97.46% classification accuracy and competitive speed in regards to the analyzed data sets.

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“…In [46], CNN cooperated with the difference of Gaussian (DoG) to recognize the target. In [47], a compact and fully CNN was trained with synthetic data because of the shortage of infrared data. The trained network was used to address target recognition in an infrared defense system.…”

Section: Dcnn-based Target Recognitionmentioning

confidence: 99%

Target Recognition in Infrared Circumferential Scanning System via Deep Convolutional Neural Networks

Chen

2020

Sensors

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With an infrared circumferential scanning system (IRCSS), we can realize long-time surveillance over a large field of view. Recognizing targets in the field of view automatically is a crucial component of improving environmental awareness under the trend of informatization, especially in the defense system. Target recognition consists of two subtasks: detection and identification, corresponding to the position and category of the target, respectively. In this study, we propose a deep convolutional neural network (DCNN)-based method to realize the end-to-end target recognition in the IRCSS. Existing DCNN-based methods require a large annotated dataset for training, while public infrared datasets are mostly used for target tracking. Therefore, we build an infrared target recognition dataset to both overcome the shortage of data and enhance the adaptability of the algorithm in various scenes. We then use data augmentation and exploit the optimal cross-domain transfer learning strategy for network training. In this process, we design the smoother L1 as the loss function in bounding box regression for better localization performance. In the experiments, the proposed method achieved 82.7 mAP, accomplishing the end-to-end infrared target recognition with high effectiveness on accuracy.

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CNN-Based Target Recognition and Identification for Infrared Imaging in Defense Systems

Cited by 37 publications

References 39 publications

The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN

The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN

Classification of Infrared Objects in Manifold Space Using Kullback-Leibler Divergence of Gaussian Distributions of Image Points

Target Recognition in Infrared Circumferential Scanning System via Deep Convolutional Neural Networks

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