VH-HFCN based Parking Slot and Lane Markings Segmentation on Panoramic Surround View
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The automatic parking is being massively developed by car manufacturers and providers. The key to this system is the robust detection of parking slots and road structures, such as lane markings. In this paper, we proposed an HFCN-based segmentation method for parking slot and lane markings in a panoramic surround view (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 a highly fused convolutional network (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 evaluate our model on the PSV dataset and the results show 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.
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