Infrared target detection and recognition in complex scene

The mainstream target detection network has outstanding target detection capability in high quality RGB images, but for infrared images with poor resolution, the target detection performance decreases significantly. In order to improve the performance of infrared target detection in complex scene, the following measures are adopted in this paper: Firstly, by referring to the field adaption and adopting the appropriate infrared image preprocessing means, the infrared image is closer to the RGB image, so that the mainstream target detection network can further improve the detection accuracy. Secondly, based on the one-stage target detection network YOLOv3, the algorithm replaces the original MSE loss function with the GIOU loss function. It is verified by experiments that the detection accuracy on the open infrared data set the FLIR is significantly improved. Thirdly, in view of the problem of large target size span existing in FLIR dataset, the SPP module is added with reference to the idea of the spatial pyramid to enrich the expression ability of feature map, expand the receptive field of feature map, and further improve the accuracy of target detection.