Research On Infrared Vehicle Target Recognition

In 21 century, infrared technology is valuable in military and civilian domains. With the demand of battlefield in the future and the development of country economy, the infrared technology will be more and more important. Infrared target recognition is one of the hot problems for scholars in the world. Based on the features of infrared automatic target recognition, this paper has made some systematic studies in infrared vehicle target recognition, such as preprocessing of infrared vehicle target images, contrast enhancement, automatic segmentation, features extraction and target recognition, etc.. The main works of this paper are as follows:1) Adaptive fuzzy infrared image enhancement based on genetic algorithm. Because there are uncertainties, that is to say, fuzziness in the infrared vehicle target image, fuzzy theory is used in the infrared image processing. A new kind of image measure function is presented by fuzzy theory. We use it as the fitness function of genetic algorithm to adaptively optimize parameterαandβin in-complete Beta function. Thus an optimal gray transformation curve is obtained to enhance the region of interest in an infrared vehicle target image. Experimental results show that this method has high adaptability and intelligence. The proposed method is better than classical image enhancement methods and some existing similar methods.2) Automatic fuzzy segmentation for infrared vehicle target based on genetic algorithm. According to the characteristic of infrared images, a new auto(?)atic fuzzy segmentation method is presented based on genetic algorithm to segment vehicle target from an infrared image. Firstly, a region of interest (ROI) is selected in order to reduce computation cost. Secondly, the ROI is enhanced by fuzzy algorithm. Thirdly, 2D Maximum Between-cluster Variance algorithm is applied to segment the ROI. At the same time, the genetic algorithm is combined with 2D MBV to make the calculation faster by its capacity of searching the best answer in a threshold space. Then we detect fuzzy edge based on shortening width of fuzzy edge. The final segmentation image can be obtained by OR and filling operations for the segmented region by combining 2D MBV with fuzzy edge. Exper(?)mental results show that the new method can get higher well and truly vehicle target than 1D OTSU or 2D OTSU.3) Infrared vehicle target recognition based on RBF network. A vehicle recognition approach is proposed based on radial basis function (RBF) neural Network. It extracts the invariant features for translation, rotation, and scale change of vehicle targets: 8 discrete cosine transformation descriptors, 6 independent invariant moments and 3 region characterizations. Compared with BP network, the RBF Network is faster and has higher recognition rate.