Demir, H. SeckinAkagunduz, Erdem2021-06-112021-06-112020Demir, H. Seçkin; Akagündüz, Erdem (2020). "Filter design for small target detection on infrared imagery using normalized-cross-correlation layer", Turkish Journal of Electrical Engineering & Computer Sciences, Vol. 28, no. 1, pp. 302-317.1300-06321303-6203https://doi.org/10.3906/elk-1807-287In this paper, we introduce a machine learning approach to the problem of infrared small target detection filter design. For this purpose, similar to a convolutional layer of a neural network, the normalized-cross-correlational (NCC) layer, which we utilize for designing a target detection/recognition filter bank, is proposed. By employing the NCC layer in a neural network structure, we introduce a framework, in which supervised training is used to calculate the optimal filter shape and the optimum number of filters required for a specific target detection/recognition task on infrared images. We also propose the mean-absolute-deviation NCC (MAD-NCC) layer, an efficient implementation of the proposed NCC layer, designed especially for FPGA systems, in which square root operations are avoided for real-time computation. As a case study we work on dim-target detection on midwave infrared imagery and obtain the filters that can discriminate a dim target from various types of background clutter, specific to our operational concept.eninfo:eu-repo/semantics/openAccessSmall Target DetectionFilter DesignNormalized-Cross-CorrelationConvolutional Neural NetworksArticle28130231710.3906/elk-1807-2872-s2.0-85079838212WOS:000510459900022334646Q4Q3