  The automatic parking is being massively developed by car manufacturers and
providers. Until now, there are two problems with the automatic parking. First,
there is no openly-available segmentation labels of parking slot on panoramic
surround view (PSV) dataset. Second, how to detect parking slot and road
structure robustly. Therefore, in this paper, we build up a public PSV dataset.
At the same time, we proposed a highly fused convolutional network (HFCN) based
segmentation method for parking slot and lane markings based on the PSV
dataset. A surround-view image is made of four calibrated images captured from
four fisheye cameras. We collect and label more than 4,200 surround view images
for this task, which contain various illuminated scenes of different types of
parking slots. A VH-HFCN network is proposed, which adopts an HFCN as the base,
with an extra efficient VH-stage for better segmenting various markings. The
VH-stage consists of two independent linear convolution paths with vertical and
horizontal convolution kernels respectively. This modification enables the
network to robustly and precisely extract linear features. We evaluated our
model on the PSV dataset and the results showed outstanding performance in
ground markings segmentation. Based on the segmented markings, parking slots
and lanes are acquired by skeletonization, hough line transform and line
arrangement.
