Rotational Convolution: Rethinking Convolution for Downside Fisheye Images

It has long been recognized that the standard convolution is not rotation equivariant and thus not appropriate for downside fisheye images which are rotationally symmetric. This paper introduces Rotational Convolution, a novel convolution that rotates the convolution kernel by characteristics of downside fisheye images. With the four rotation states of the convolution kernel, Rotational Convolution can be implemented on discrete signals. Rotational Convolution improves the performance of different networks in semantic segmentation and object detection markedly, harming the inference speed slightly. Finally, we demonstrate our methods’ numerical accuracy, computational efficiency, and effectiveness on the public segmentation dataset THEODORE and our self-built detection dataset SEU-fisheye. Our code is available at: https://github.com/wx19941204/Rotational-Convolution-for-downside-fisheye-images.