Research On Crucial Target Detection Technology In Specific Scene From Thermal Imagery

The research of target detection from infrared imagery in specific scene can be illustrated as follow:The thermal imaging sensor is utilized to capture the information of target or background from actual scenes such as low sky, deep sky, sea-sky as well as land background; Then the signal and image processing theorys are combined with the artificial intelligence technology to analyze the scene information automatically and detect infrared targets of interest (eg. spy plane, missile, vehicle, ship, wrecked people and so on), whose some qualitative and quantitative features can be obtained; With these information, targets can be recognized onlinely and specified targets can also be locked, so as to realize the automatic intelligent surveillance.Nowadays infrared target detecion is a very active research area in computer vision. As the infrared target detection in complex scenes presents a low detection index, while it has high application value extremely, more and more researchers pay attention in this topic. Acoording to the investigation, in views of various complex infrared scenes, this thesis researchs three aspects of infared target detection:dim target detection in heavy clutter based on far-distance IR imagery, target detection in complex outdoors scene based on infared survlliance, and target detection in complex outdoors scene based on thermal-visible survlliance. The main contributions of this thesis can be concluded as follows.1、Aim at the complex far-distance sky background, this thesis presents a novel enhancement approach for infrared dim target based on local analysis mechanism, which is capable of suppressing background clutters and highlighting targets. It studies the fractional integral theory, and analyzes its character in the frequency domain. Then according to features of infrared image, the fractional integral is utilized to enhance the infared dim target. Experiment results show that the proposed approach is capable of eliminating the background clutters and enhancing the signal-to-noise ratio effectively.2、Aim at the complex far-distance sky background, in view of the background information, a novel infrared dim target detection approach is presented, which is based on background suppression by artificial immune network (aiNet) and threshold segmentation by k-means cluster of rows and columns. Firstly, the aiNet is combined with Robinson guard to build the adaptive local spatial background models as fuzzy topological memory antibody bank. In the process of antibody bank modeling, a series of antibody evolution strategies are designed based on self-organizing map (SOM). With these models, background clutters are suppressed according to the degree of fuzzy match between pixels and models. Then the proposed adaptive segmentation algorithm based on k-means cluster of rows and columns is utilized to detect the true targets. Experimental results show that the F1measurement of the proposed approach is up to99%. The proposed approach is able to build the spatial background models adaptively according to the local change of image, and suppress the background clutters and highlight targets effectively. It is capable of improving the signal-to-noise ratio of images and detecting targets effectively.3、Aim at the complex far-distance sky background, in view of the dim target information, a novel infrared dim target detection approach is presented, which is based on background suppression by Fuzzy adaptive resonance theory (Fuzzy-ART) and threshold segmentation by adaptive segmentation algorithm based on fuzzy cluster of rows and columns. Firstly, the infrared dim target training set is simulated according to the principle of thermal imagery. Then a Fuzzy-ART neural network is utilized to build the target models. With these models, the background clutters are suppressed according to the degree of fuzzy match between pixels and models. Lastly, the adaptive segmentation algorithm based on fuzzy cluster of rows and columns is utilized to detect the true targets. Experimental results show that the proposed approach is able to suppress background clutters and highlight targets effectively. It is capable of improving the signal-to-noise ratio of images and detecting targets effectively.4、Aim at the complex outdoors surveillance scenes, a detection framework for infrared moving target is builded. In view of statistical classification, this thesis presents a multi-stage classification approach to detect the target based on a spatial-temporal detection framework:background extraction, background suppression, background model, target location and target detection. At first, a multi-level spatial-temporal median filter is utilized to extract the background frame, with which the background clutters are suppressed by using the principal component analysis technique. A spatially related Fuzzy ART neural network is then applied to identify the local regions-of-interest (ROI). Within each region, another Fuzzy ART neural network is utilized to detect the target. Lastly, a binary constrained texture-based active contour model is applied to extract each continuous silhouette. Experimental results demonstrate that the proposed approach is capable of detecting infrared moving targets and extracting the silhouettes effectively for F1measurement up to96.3%.5、Aim at the complex outdoors surveillance scenes, the thermal and visible imagery are combined to detect the target. In order to solve the narrow applicable range, the heavy clutters as well as the low detection ratio problems more effectively, this thesis presents a novel target detection approach in thermal-visible surveillance based on multiple-valued immune network. Firstly, two Fuzzy ART neural networks are utilized to build the background models of thermal and visible components. Then according to the multiple-valued immune network model, a series of immune response strategies are designed to cooperate B cell with T cell to build the interactive model, which takes the infrared background model as B cell, the visible background model as T cell. With the interactive model, the targets are detected according to the degree of fuzzy match between pixels and models. Experimental results show that the Fl measurement of the proposed approach is up to96.4%. It is able to complement information between thermal and visible components effectively. The proposed approach is capable of detecting targets in complex scenes effectively.