Fast and accurate detection of maneuvering armored targets is an important performance requirement for low altitude unmanned aerial vehicles, but the rotation invariance of the current mainstream detection methods is not enough to deal with the challenge effectively. Combined with deep convolution neural network (CNN), we propose a low altitude armored target detection method based on rotation invariant Faster R-CNN. This method introduces the rotation invariant layer on the basis of the original frame of Faster R-CNN to strengthen the invariance of the target′s CNN feature before and after rotation by adding regularization constraints on the objective function of the model. In the experiment, three typical models of armored target are selected to simulate the low altitude reconnaissance environment under different scenes indoors and outdoors, reconnaissance simulated images of the targets are used as sample data for model verification, which are obtained by using a polarizing hyperspectral camera. In the multi model comparison test, the improved model increases the mean average precision by 2.4% on the original basis and achieves the best test result, which preliminary verifies the effectiveness of the improved method.