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.

自动驾驶中的物体检测是一个成熟的问题，但对于用于环视近场感测的鱼眼相机，物体检测相对较少探索。我们设计了旋转边界框、椭圆、通用多边形等表示方式，并提出了实例分割的 mIOU 指标来分析这些表示方式。提出的 FisheyeDetNet 模型使用多边形优于其他方法，在 Valeo 鱼眼环视数据集上达到了 49.5％的 mAP 得分。据我们所知，这是关于自动驾驶场景下鱼眼相机物体检测的首个详细研究。

FisheyeDetNet: 自动驾驶中的鱼眼环视摄像头物体检测

FisheyeDetNet: Object Detection on Fisheye Surround View Camera Systems  for Automated Driving

Object detection is a core problem in computer vision. With the development
of deep ConvNets, the performance of object detectors has been dramatically
improved. The deep ConvNets based object detectors mainly focus on regressing
the coordinates of bounding box, e.g., Faster-R-CNN, YOLO and SSD. Different
from these methods that considering bounding box as a whole, we propose a novel
object bounding box representation using points and links and implemented using
deep ConvNets, termed as Point Linking Network (PLN). Specifically, we regress
the corner/center points of bounding-box and their links using a fully
convolutional network; then we map the corner points and their links back to
multiple bounding boxes; finally an object detection result is obtained by
fusing the multiple bounding boxes. PLN is naturally robust to object occlusion
and flexible to object scale variation and aspect ratio variation. In the
experiments, PLN with the Inception-v2 model achieves state-of-the-art
single-model and single-scale results on the PASCAL VOC 2007, the PASCAL VOC
2012 and the COCO detection benchmarks without bells and whistles. The source
code will be released.

使用深度卷积神经网络实现了基于点和连接线的物体包围框表示方法 Point Linking Network（PLN），相比现有算法更具鲁棒性，可以应对物体遮挡、比例和长宽比等变化，且在多个基准测试数据集上均取得了最先进的单模型、单尺度检测结果。