  Convolutional Neural Networks (CNNs) perform very well in image
classification and object detection in recent years, but even the most advanced
models have limited rotation invariance. Known solutions include the
enhancement of training data and the increase of rotation invariance by
globally merging the rotation equivariant features. These methods either
increase the workload of training or increase the number of model parameters.
To address this problem, this paper proposes a module that can be inserted into
the existing networks, and directly incorporates the rotation invariance into
the feature extraction layers of the CNNs. This module does not have learnable
parameters and will not increase the complexity of the model. At the same time,
only by training the upright data, it can perform well on the rotated testing
set. These advantages will be suitable for fields such as biomedicine and
astronomy where it is difficult to obtain upright samples or the target has no
directionality. Evaluate our module with LeNet-5, ResNet-18 and tiny-yolov3, we
get impressive results.
