为了提升实际作战环境下目标检测识别的性能，本文提出了一种基于红外显著性目标导引的改进YOLO（You Only Look Once）网络的智能装备目标识别算法，该算法利用红外图像提供目标可能的位置引导可见光图像中的深度自主学习，提升检测与识别的实时性。改进YOLO-V3 识别网络是以Darknet-53 为基础网络架构，利用Dense Net 代替具有较低分辨率的原始转移层，同时采用分类网络预训练、多尺度检测网络训练等措施增强特征传播，复用和融合的性能。仿真实验结果表明，本文提出的模型可以有效地提高现有目标检测与识别的性能。

To improve the performance of object detection and recognition in a real-world combat environment, an improved intelligent object recognition algorithm based on infrared saliency object guidance is proposed. It uses the object information in an infrared image to guide deep self-learning in vision images. The improved YOLO-V3 recognition network is based on the Darknet-53 network architecture, using DenseNet instead of the original transfer layer with lower resolution. Classification network pretraining, multiscale detection network training, and other measures are used to enhance feature propagatio n and reuse and fusion performance. Simulation results show that the proposed model can effectively improve the performance of existing object detection and recognition networks.