Abstract
The method of infrared small target detection is a crucial technology for infrared early-warning tasks, infrared imaging guidance, and large field of view target monitoring, and it is very important for certain early-warning tasks. In this paper, we propose an end-to-end infrared small target detection model (called CDAE) based on denoising autoencoder network and convolutional neural network, which treats small targets as “noise” in infrared images and transforms small target detection tasks into denoising problems. In addition, we use the perceptual loss to solve the problem of background texture feature loss in the encoding process, and propose the structural loss to make up for the perceptual loss defect in which small targets appear. We compare ten methods on six sequences and one single-frame dataset. Experimental results show that our method obtains the highest SCRG value on four sequences and the highest BSF value on six sequences. From the ROC curve, we can see that our method achieves the best results in all test sets.
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Lu H, Li Y, Mu S, Wang D, Kim H, Serikawa S (2018) Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet Things J 5(4):2315–2322
Chen CLP, Li H, Wei Y, Xia T, Tang YY (2014) A local contrast method for small infrared target detection. IEEE Trans Geosci Remote Sens 52(1):574–581
Qin Y, Li B (2016) Effective infrared small target detection utilizing a novel local contrast method. IEEE Geosci Remote Sens Lett 13(12):1890–1894
Chen Y, Xin Y (2016) An efficient infrared small target detection method based on visual contrast mechanism. IEEE Geosci Remote Sens Lett 13(7):962–966
Wei Y, You X, Li H (2016) Multiscale patch-based contrast measure for small infrared target detection. Pattern Recognit 58:216–226
Shi Y, Wei Y, Yao H, Pan D, Xiao G (2018) High-boost-based multiscale local contrast measure for infrared small target detection. IEEE Geosci Remote Sens Lett 15(1):33–37
Deng H, Sun X, Liu M, Ye C, Zhou X (2016) Small infrared target detection based on weighted local difference measure. IEEE Trans Geosci Remote Sens 54(7):4204–4214
Redmon J, Divvala S, Girshick R, et al (2016) You only look once: unified, real-time object detection. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE Computer Society
Serikawa S, Lu H (2014) Underwater image dehazing using joint trilateral filter. Comput Electr Eng 40 (1):41–50
Lu H, Li Y, Uemura T, Kim H, Serikawa S (2018) Low illumination underwater light field images reconstruction using deep convolutional neural networks. Futur Gener Comput Syst 82:142–148
Deshpande SD, Er MH, Ronda V, Chan P (1999) Max-mean and max-median filters for detection of small targets. In: Proceedings of the SPIE’s international symposium on optical science, engineering, and instrumentation, international society for optics and photonics, Denver, CO, USA, 4 October, pp 74–83
Zeng M, Li J, Peng Z (2006) The design of top-hat morphological filter and application to infrared target detection. Infrared Physics Technol 48:67–76
Yang C, Ma J, Qi S, et al (2015) Directional support value of Gaussian transformation for infrared small target detection[J]. Applied Optics 54–9
Chen CLP, Li H, Wei Y, Xia T, Tang YY (2014) A local contrast method for small infrared target detection. IEEE Trans Geosci Remote Sens 52(1):574–581
Han J, Ma Y, Zhou B, Fan F, Liang K, Fang Y (2014) A robust infrared small target detection algorithm based on human visual system. IEEE Geosci Remote Sens Lett 11(12):2168– 2172
Chen Y, Xin Y (2016) An effcient infrared small target detection method based on visual contrast mechanism. IEEE Geosci Remote Sens Lett 13(7):962–966
Bai K, Wang Y, Song Q (2016) Patch similarity based edge-preserving background estimation for single frame infrared small target detection. In: 2016 IEEE international conference on image processing (ICIP). IEEE, p 2016
Shao X, Fan H, Lu G, Xu J (2012) An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system. Infr Phys Technol 55(5):403– 408
Han J, Ma Y, Huang J, Mei X, Ma J (2016) An infrared small target detecting algorithm based on human visual system. IEEE Geosci Remote Sens Lett 13(3):452–456
Han J, Ma Y, Zhou B, Fan F, Liang K, Fang Y (2014) A robust infrared small target detection algorithm based on human visual system. IEEE Geosci Remote Sens Lett 11(12):2168–2172
Chenqiang G, Deyu M, Yi Y, et al (2013) Infrared patch-image model for small target detection in a single image. IEEE Trans Image Process 22(12):4996–5009
Yimian D, Yiquan W, Yu S (2016) Infrared small target and background separation via column-wise weighted robust principal component analysis. Infrared Phys Technol 77:421–430
Yimian D, Yiquan W (2016) Reweighted infrared patch-tensor model with both non-local and local priors for single-frame small target detection. IEEE journal of selected topics in applied earth observations and remote sensing
Chen B, Wang W, Qin Q (2010) Infrared dim target detection based on fuzzy-ART neural network[C]. In: International conference on computational intelligence & natural computing. IEEE
Liangkui L, Shaoyou W, et al (2018) Using deep learning to detect small targets in infrared oversampling images. J Syst Eng Electron (5):947–952
Gao C, Wang L, Xiao Y, Zhao Q, Meng D (2018) Infrared small-dim target detection based on Markov random field guided noise modeling. Pattern Recogn 76:463–475
Girshick R (2015) Fast r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp 1440–1448
Ren S, He K, Girshick R, et al (2015) Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, pp 91–99
Vincent P, Larochelle H, Bengio Y, et al (2008) Extracting and composing robust features with denoising autoencoders. International conference on machine learning, ACM
Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: European conference on computer vision. Springer, Cham, pp 694–711
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The paper is supported by National Natural Science Foundation of China (61703209,61773215).
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Shi, M., Wang, H. Infrared Dim and Small Target Detection Based on Denoising Autoencoder Network. Mobile Netw Appl 25, 1469–1483 (2020). https://doi.org/10.1007/s11036-019-01377-6
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DOI: https://doi.org/10.1007/s11036-019-01377-6