MORPHOLOGY INFRARED IMAGE TARGET DETECTION ALGORITHM OPTIMIZED BY GENETIC THEORY

This paper proposes a novel morphology algorithm for target detection of the infrared images, which is optimized by the genetic algorithm. First, several improvements have been adopted for genetic algorithm (GA). The improvements include the auto-regulation of genetic evolution crossover probability and mutation probability which based on population difference; A more reasonable target characteristic variable according the feature of infrared image has been designed to train structure elements. So the efficiency of the algorithm can be improved obviously, at same time, the optimized trained structural elements could reflect the sample’s true structure information. Then the background and objectives structural elements could be got by inputting background and target samples into the GA model. Using it as a priori knowledge of the morphology operation, it does favor to improve the algorithm’s accuracy and adaptability. .Experiment shows that this method can achieve a higher detection efficiency and accuracy.