Abstract
Convolutional neural network (CNN) is an efficient algorithm in deep learning. Aiming at the field of military target recognition, this paper constructs a dataset for military target recognition in battlefield, which contains ten kinds of targets. The characteristics of the dataset are described and analyzed. Three classical CNN models (AlexNet, VGGNet and ResNet) and two learning strategies (dropout and data augmentation) are evaluated on the dataset. Under the same condition of the dataset and the same super-parameter setting, the effects of different models are presented and analyzed. The experimental results show that the mean average precisions of ResNet and VGGNet are better than AlexNet, and the accuracies of both ResNet and VGGNet are over 90% with only thousands of training images. At the same time, the dropout and data augmentation strategies have a strong effect for improving the performance.
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References
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Krizhevsky, A., Sutskever, I., Hinton, G.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)
Russakovsky, O., Deng, J., Su, H., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. CoRR abs/1512.03385 (2015)
Loffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of 32th International Conference on Machine Learning, pp. 448–456 (2015)
Li, Y., Wang, N., Shi, J., et al.: Adaptive batch normalization for practical domain adaptation. Pattern Recogn. 80, 109–117 (2016)
Yosinski, J., Clune, J., Bengo, Y., et al.: How tansferable are features in deep neural networks? Adv. Neural. Inf. Process. Syst. 27, 3320–3328 (2014)
Srivastava, N., Hinton, G., Krizhevsky, A., et al.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Perez, L., Wang, J.: The effectiveness of data augmentation in image classification using deep learning. CoRR abs/1712.04621 (2017)
Sajjad, M., Khan, S., Muhammad, K., et al.: Multi-grade brain tumor classification using deep CNN with extensive data augmentation. J. Comput. Sci. 30, 174–182 (2019)
Zheng, L., Shen, L., Tian, L., et al.: Scalable person re-identification: a benchmark. In: IEEE International Conference on Computer Vision, pp. 1116–1124 (2015)
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Ding, X., Xing, L., Lin, T., Wang, J., Li, Y., Miao, Z. (2019). Evaluating CNNs for Military Target Recognition. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_59
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DOI: https://doi.org/10.1007/978-3-030-26969-2_59
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