Great progress has been achieved in the community of autonomous driving in the past few years. As a safety-critical problem, however, anomaly detection is a huge hurdle towards a large-scale deployment of autonomous vehicles in the real world. While many approaches, such as uncertainty estimation or segmentation-based image resynthesis, are extremely promising, there is more to be explored. Especially inspired by works on anomaly detection based on image resynthesis, we propose a novel approach for anomaly detection through style transfer. We leverage generative models to map an image from its original style domain of road traffic to an arbitrary one and back to generate pixelwise anomaly scores. However, our experiments have proven our hypothesis wrong, and we were unable to produce significant results. Nevertheless, we want to share our findings, so that others can learn from our experiments.
翻译:过去几年来,自主驾驶社区取得了巨大进步。然而,作为一个安全的关键问题,异常点探测是大规模在现实世界部署自主车辆的巨大障碍。许多方法,如不确定性估计或基于分割图像的再合成,都非常有希望,但还有更多的问题有待探索。特别是,根据图像再合成的异常点探测工作,我们提出了通过风格传输探测异常点的新办法。我们利用基因模型将图像从原来的公路交通风格领域映射为任意领域,然后回溯到产生像素的异常分数。然而,我们的实验证明我们的假设是错误的,我们未能产生显著的结果。然而,我们想分享我们的调查结果,以便其他人能够从我们的实验中学习。