text: For the domain of self-driving and automatic parking, perception is a basic and critical technique, moreover, the detection of lane markings and parking slots is an important part of visual perception. Compared with front sight images, panoramic images(PI) can capture more comprehensive pavement information. However, the imbalance of different classes in PI is even more serious. Additionally, the judgment of boundary information between areas is a hard problem in deep models. Therefore, we propose a new model named DFNet to solve these problems. The proposed model has two main contributions, one is dynamic loss weights, and the other is residual fusion block(RFB). DFNet use dynamic loss weights to overcome the negative effect of imbalance dataset, which are calculated according to the pixel number of each class in a batch. RFB is composed of several convolutional layers, a pooling layer, and a fusion layer to combine the feature maps by pixel multiplication, which can reduce boundary information loss. We evaluate our method on PSV dataset, and the achieved advanced results demonstrate the effectiveness of the proposed model.
  @cite_14: 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.
summary: Traditional image processing methods on panoramic images and deep learning methods on front sight images both achieve excellent results, but few work is aimed to use deep leaning on panoramic images. This is because the accuracy of deep learning model is largely related to datasets, and there are few public dataset of panoramic images which can be used to train a model. The first public panoramic dataset for lane markings and parking slots is panoramic surround view (PSV) dataset, released by Yan @cite_14 . And this dataset is specially used for semantic segmentation, which is labeled pixel by pixel, and each pixel has its corresponding class. In order to combine the advantages of panoramic images and deep learning, we use the semantic segmentation method with convolutional network to segment the area and classify the class of lane makings and parking slots on the PSV dataset.