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.
In @cite_14 , they achieved the segmentation of lane markings and parking slots using semantic segmentation method on PSV dataset, and proposed a VH-stage module special for linear structures. But the size of their model is too large to meet the requirement of using in embedded and mobile platform. In this paper, we put forward a smaller size model, but with higher accuracy. And the two main improved methods we proposed are proved to be significant.
@cite_14
2798039499 2020206277 1682148205 2809446072
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. This paper addresses the problem of semantic part parsing (segmentation) of cars, i.e.assigning every pixel within the car to one of the parts (e.g.body, window, lights, license plates and wheels). We formulate this as a landmark identification problem, where a set of landmarks specifies the boundaries of the parts. A novel mixture of graphical models is proposed, which dynamically couples the landmarks to a hierarchy of segments. When modeling pairwise relation between landmarks, this coupling enables our model to exploit the local image contents in addition to spatial deformation, an aspect that most existing graphical models ignore. In particular, our model enforces appearance consistency between segments within the same part. Parsing the car, including finding the optimal coupling between landmarks and segments in the hierarchy, is performed by dynamic programming. We evaluate our method on a subset of PASCAL VOC 2010 car images and on the car subset of 3D Object Category dataset (CAR3D). We show good results and, in particular, quantify the effectiveness of using the segment appearance consistency in terms of accuracy of part localization and segmentation. In this paper we propose a method to generate a large scale and accurate dense 3D semantic map of street scenes. A dense 3D semantic model of the environment can significantly improve a number of robotic applications such as autonomous driving, navigation or localisation. Instead of using offline trained classifiers for semantic segmentation, our approach employs a data-driven, nonparametric method to parse scenes which easily scale to a large environment and generalise to different scenes. We use stereo image pairs collected from cameras mounted on a moving car to produce dense depth maps which are combined into a global 3D reconstruction using camera poses from stereo visual odometry. Simultaneously, 2D automatic semantic segmentation using a nonparametric scene parsing method is fused into the 3D model. Furthermore, the resultant 3D semantic model is improved with the consideration of moving objects in the scene. We demonstrate our method on the publicly available KITTI dataset and evaluate the performance against manually generated ground truth. Semantic segmentation is a challenging problem in computer vision. Many applications, such as autonomous driving and robot navigation with urban road scene, need accurate and efficient segmentation. Most state-of-the-art methods focus on accuracy, rather than efficiency. In this paper, we propose a more efficient neural network architecture, which has fewer parameters, for semantic segmentation in the urban road scene. An asymmetric encoder–decoder structure based on ResNet is used in our model. In the first stage of encoder, we use continuous factorized block to extract low-level features. Continuous dilated block is applied in the second stage, which ensures that the model has a larger view field, while keeping the model small-scale and shallow. The down sampled features from encoder are up sampled with decoder to the same-size output as the input image and the details refined. Our model can achieve end-to-end and pixel-to-pixel training without pretraining from scratch. The parameters of our model are only @math , @math less than those of others such as SegNet, etc. Experiments are conducted on five public road scene datasets (CamVid, CityScapes, Gatech, KITTI Road Detection, and KITTI Semantic Segmentation), and the results demonstrate that our model can achieve better performance.