Object detection is a mature problem in autonomous driving with pedestrian detection being one of the first deployed algorithms. It has been comprehensively studied in the literature. However, object detection is relatively less explored for fisheye cameras used for surround-view near field sensing. The standard bounding box representation fails in fisheye cameras due to heavy radial distortion, particularly in the periphery. To mitigate this, we explore extending the standard object detection output representation of bounding box. We design rotated bounding boxes, ellipse, generic polygon as polar arc/angle representations and define an instance segmentation mIOU metric to analyze these representations. The proposed model FisheyeDetNet with polygon outperforms others and achieves a mAP score of 49.5 % on Valeo fisheye surround-view dataset for automated driving applications. This dataset has 60K images captured from 4 surround-view cameras across Europe, North America and Asia. To the best of our knowledge, this is the first detailed study on object detection on fisheye cameras for autonomous driving scenarios.

翻译：目标检测是自动驾驶中一个成熟的问题，行人检测是首批部署的算法之一，已在文献中得到全面研究。然而，针对环视近场感知所用的鱼眼相机，目标检测的研究相对较少。由于鱼眼相机存在严重的径向畸变（尤其在图像边缘区域），标准边界框表示方法无法适用。为解决这一问题，我们探索了扩展标准目标检测输出中边界框表示的方法。我们设计了旋转边界框、椭圆、通用多边形作为极坐标弧/角表示，并定义了实例分割mIOU指标来分析这些表示。所提出的FisheyeDetNet模型采用多边形表示，性能优于其他模型，在Valeo鱼眼环视自动驾驶数据集中取得了49.5%的mAP分数。该数据集包含来自欧洲、北美和亚洲的4个环视摄像头采集的6万张图像。据我们所知，这是首个针对自动驾驶场景下鱼眼相机目标检测的详细研究。