Recognizing targets from infrared images is a very important task for defense system. Recently, deep learning becomes an important solution of the classification problems which can be used for target recognition. In this study, a machine learning approach SVM and a deep learning approach CNN are compared for target recognition on infrared images. This paper applies SVM to measure the linear separability of the classes and obtain the baseline performance for the classes. Then, the constructed CNN model is applied to the dataset. The experimental results show that CNN model increases the overall performance around % 7.7 than SVM on prepared infrared image datasets.
Automatic detection of targets from far distances is a very challenging problem. Background clutter and small target size
are the main difficulties which should be solved while reaching a high detection performance as well as a low
computational load. The pre-processing, detection and post-processing approaches are very effective on the final results.
In this study, first of all, various methods in the literature were evaluated separately for each of these stages using the
simulated test scenarios. Then, a full system of detection was constructed among available solutions which resulted in
the best performance in terms of detection. However, although a precision rate as 100% was reached, the recall values
stayed low around 25-45%. Finally, a post-processing method was proposed which increased the recall value while
keeping the precision at 100%. The proposed post-processing method, which is based on local operations, increased the
recall value to 65-95% in all test scenarios.
Pre-processing, thresholding and post-processing stages are very important especially for very small target detection from infrared images. The effects of these stages to the final detection performance are measured in this study. Various methods for each stage are compared based on the final detection performance, which is defined by precision and recall values. Among various methods, the best method for each stage is selected and proved. For the pre-processing stage, local block based methods perform the best, nearly for all thresholding methods. The best thresholding method is chosen as the one, which does not need any user defined parameter. Finally, the post processing method, which is suitable for the best performing pre-processing and thesholding methods is selected.
Top-Hat transform is well known background suppression method used in small target detection. In this paper, we investigate various different Top-Hat transformation based small target detection approaches. All of the methods are implemented with their best parameter settings and applied to the same test image. The comparison among them is done in terms of three issues: 1. the detection performance (precision and false alarm rate), 2. the time requirement of the method and its usability for real time applications, 3. the number of parameters, which need user interaction. Results show that all of the algorithms require a prior knowledge of target size, which is either used as the structuring element size or as the threshold for post-processing. Algorithms, which use automatic approaches to select its parameters, are not generic to be applied to various images. But algorithms, which use adaptive methods for deciding on the threshold value, perform better than the others.