Object detection is a comprehensively studied problem in autonomous driving. However, it has been relatively less explored in the case of fisheye cameras. The strong radial distortion breaks the translation invariance inductive bias of Convolutional Neural Networks. Thus, we present the WoodScape fisheye object detection challenge for autonomous driving which was held as part of the CVPR 2022 Workshop on Omnidirectional Computer Vision (OmniCV). This is one of the first competitions focused on fisheye camera object detection. We encouraged the participants to design models which work natively on fisheye images without rectification. We used CodaLab to host the competition based on the publicly available WoodScape fisheye dataset. In this paper, we provide a detailed analysis on the competition which attracted the participation of 120 global teams and a total of 1492 submissions. We briefly discuss the details of the winning methods and analyze their qualitative and quantitative results.

本文介绍了2022年CVPR OmniCV工作坊上，我们提出的针对鱼眼摄像头目标检测的WoodScape比赛，共有来自全球120个队伍的1492个提交。我们对赢家的方法进行了定性和定量分析，并详细讨论了比赛的细节。

用于自动驾驶的林景微缩鱼眼物体检测--CVPR 2022 OmniCV 工作坊挑战赛