Multi-dimensional feature point space infrared dim target detection method

As artificial intelligence algorithm was introduced into target detection, the detection of spatial infrared dim targets could be classified as the binary problem of fuzzy detection. According to the detection model of infrared dim target in the air, a signal voltage ratio spectrum model was established. The simulation analysis showed that the variation trend of voltage ratio was related to the speed, attitude of the target and the two-machine posture, which could be used to detect the target. The dynamic characteristics building theory was adopted to build the bicolor ratio feature space of infrared dim target. Based on this feature space, the least squares classification algorithm was optimized to identify the objects from the spectral signal hierarchy. This method not only reduces the amount of the sample data, but also prevents the phenomenon of over-fitting caused by the parameter selection of Gaussian kernel function. It ensures the classification accuracy and improves the classification efficiency nearly doubled. Reference basis is provided for infrared dim target detection by artificial intelligence algorithm.