Object Detection in Omnidirectional Images Based on Spherical CNN

Omnidirectional cameras are gaining popularity in VR/AR applications and autonomous driving due to their wide field of view. However, the images produced by the cameras have geometric distortions especially in the polar regions. This distortion poses a great challenge to computer vision tasks such as object detection. In this paper, we propose a CNN architecture called spherical CNN which is designed for omnidirectional images. According to the mapping relationship between the sphere and plane, our spherical CNN changes the size of convolution kernel and the locations of sampling points at different latitudes to adapt the image distortion. In order to verify the effectiveness of spherical CNN for the omnidirectional image object detection task, it is applied to detection network SSD(Single Shot MultiBox Detector). In our experiments, we achieve a 2% improvement on the mAP75 which represents the accuracy of detection. The experimental results verify that spherical CNN can improve the detection performance for omnidirectional images.