论文标题
自动驾驶的木观鱼眼检测-CVPR 2022 OMNICV Workshop挑战
Woodscape Fisheye Object Detection for Autonomous Driving -- CVPR 2022 OmniCV Workshop Challenge
论文作者
论文摘要
对象检测是自动驾驶中的一个全面研究的问题。但是，在鱼眼相机的情况下，它的探索相对较少。强烈的径向失真破坏了卷积神经网络的翻译不变性电感偏差。因此，我们提出了自动驾驶的木观鱼眼检测挑战，这是CVPR 2022年全向计算机视觉（OMNICV）的一部分。这是针对鱼眼相机对象检测的首批比赛之一。我们鼓励参与者设计在不纠正的情况下在鱼眼图像上本地工作的模型。我们使用Codalab根据公开可用的Fisheye数据集主持竞争。在本文中，我们提供了有关竞争的详细分析，该分析吸引了120个全球团队的参与和1492份提交的参与。我们简要讨论获胜方法的细节，并分析其定性和定量结果。
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.
