7 research outputs found
Fast and Robust Small Infrared Target Detection Using Absolute Directional Mean Difference Algorithm
Infrared small target detection in an infrared search and track (IRST) system
is a challenging task. This situation becomes more complicated when high
gray-intensity structural backgrounds appear in the field of view (FoV) of the
infrared seeker. While the majority of the infrared small target detection
algorithms neglect directional information, in this paper, a directional
approach is presented to suppress structural backgrounds and develop a more
effective detection algorithm. To this end, a similar concept to the average
absolute gray difference (AAGD) is utilized to construct a novel directional
small target detection algorithm called absolute directional mean difference
(ADMD). Also, an efficient implementation procedure is presented for the
proposed algorithm. The proposed algorithm effectively enhances the target area
and eliminates background clutter. Simulation results on real infrared images
prove the significant effectiveness of the proposed algorithm.Comment: The Final version (Accepted in Signal Processing journal
Filter design for small target detection on infrared imagery using normalized-cross-correlation layer
In this paper, we introduce a machine learning approach to the problem of
infrared small target detection filter design. For this purpose, similarly 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 mid-wave infrared imagery and
obtain the filters that can discriminate a dim target from various types of
background clutter, specific to our operational concept
Infrared small-target detection based on background-suppression proximal gradient and GPU acceleration
Patch-based methods improve the performance of infrared small target detection, transforming the detection problem into a Low-Rank Sparse Decomposition (LRSD) problem. However, two challenges hinder the success of these methods: (1) The interference from strong edges of the background, and (2) the time-consuming nature of solving the model. To tackle these two challenges, we propose a novel infrared small-target detection method using a Background-Suppression Proximal Gradient (BSPG) and GPU parallelism. We first propose a new continuation strategy to suppress the strong edges. This strategy enables the model to simultaneously consider heterogeneous components while dealing with low-rank backgrounds. Then, the Approximate Partial Singular Value Decomposition (APSVD) is presented to accelerate solution of the LRSD problem and further improve the solution accuracy. Finally, we implement our method on GPU using multi-threaded parallelism, in order to further enhance the computational efficiency of the model. The experimental results demonstrate that our method out-performs existing advanced methods, in terms of detection accuracy and execution time