Infrared target recognition algorithm based on bounding box constrained spectral clustering

In the process of infrared imaging, target edge blurring is a key factor that affects the effect of infrared target recognition, and it is also the focus of infrared target recognition algorithms. Therefore, reasonable compensation of target geometric feature information in spectral images has become one of the research hotspots.The bounding box containing the geometric feature information of the target was used as a constraint condition, and the infrared spectrum image was hierarchically limited and filtered to reduce the loss of the target geometric shape data in the original image data and improve the recognizability of the target. A spectral clustering algorithm under bounding box constraints was designed. The parameter η was set to characterize the geometric information of the military vehicle target under test, and the parameter m was set to characterize the spectral feature information of the military vehicle target under test. In the experiment, a TEL-1000-MW infrared imaging spectrometer was used to obtain multi-spectral images. By changing the m and η values, the number of spectral feature values and the bounding box range were adjusted to obtain different target recognition images. Compared with the traditional method for the recognition effect of the same infrared target image, it was found that the geometric boundary information retention effect of the target image under test using the bounding box constraint was significantly better than that of the traditional method. When m=10, η=0.7, the infrared image target recognition effect was the best, and the algorithm convergence speed was also the best. It can be seen that the algorithm has high practical value in improving the ability of infrared target recognition and avoiding false targets and missed targets. © 2021, Editorial Board of Journal of Infrared and Laser Engineering. All right reserved.