针对用传统方法难以解决城市背景下红外图像多目标检测的问题，采用迁移学习技术把深度学习中可见光域的目标检测框架迁移到红外域中。利用该方法建立的模型的小目标检测性能非常好，在制作的测试集上平均精度mAP(IoU=0.50)为0.858。还对训练数据与模型检测性能之间的关系进行了初步研究。制作了大数据量和小数据量2个训练集，对模型进行训练，然后在相同的测试集上进行测试。通过小数据量训练的模型在制作的测试集上的平均精度mAP(IoU=0.50)为0.615。实验结果表明，数据的多样性、数量、质量等都会影响模型的好坏。

In order to solving the problem of multi-target detection of infrared image in urban background by traditional methods, migration learning technology is used to migrate the target detection framework of visible light in deep learning to the infrared domain. A model is built by the method. The model's small target detection performance is very good, and the average precision mAP(IoU=0.50)of the test set is 0.858 on the produced test set. A preliminary study of the relationship between training data and model detection performance was also conducted. Two training sets of large data volume and small data volume were produced, the model was trained, and then tested on the same test set. The average precision mAP(IoU=0.50)of the small data set is 0.615. The experimental results show that the diversity, quantity and quality of the data will affect the quality of the model.