Convolutional neural networks have been successfully applied to semantic segmentation problems. However, there are many problems that are inherently not pixel-wise classification problems but are nevertheless frequently formulated as semantic segmentation. This ill-posed formulation consequently necessitates hand-crafted scenario-specific and computationally expensive post-processing methods to convert the per pixel probability maps to final desired outputs. Generative adversarial networks (GANs) can be used to make the semantic segmentation network output to be more realistic or better structure-preserving, decreasing the dependency on potentially complex post-processing.
Since the evaluation of our work focuses on lane marking detection, we also discuss other related approaches for this problem, while we refer the reader to a recent survey for a broader overview @cite_15 . An example of a successful lane marking detection approach is by @cite_5 . In their work, they train a problem-specific spatial CNN and add hand-crafted post-processing. @cite_29 use extra vanishing-point labels to guide the network toward a more structurally consistent lane marking detection. Another recent example is the work by @cite_30 , in which a regular segmentation network is used to obtain lane marking prediction maps. They then train a second network to perform a constrained perspective transformation, after which curve fitting is used to obtain the final results. We compare our work in more detail to the studies above @cite_30 @cite_5 that are similarly conducted on the Tusimple challenge, in .
@cite_30 @cite_5 @cite_15 @cite_29
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In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions. We tackle rainy and low illumination conditions, which have not been extensively studied until now due to clear challenges. For example, images taken under rainy days are subject to low illumination, while wet roads cause light reflection and distort the appearance of lane and road markings. At night, color distortion occurs under limited illumination. As a result, no benchmark dataset exists and only a few developed algorithms work under poor weather conditions. To address this shortcoming, we build up a lane and road marking benchmark which consists of about 20,000 images with 17 lane and road marking classes under four different scenarios: no rain, rain, heavy rain, and night. We train and evaluate several versions of the proposed multi-task network and validate the importance of each task. The resulting approach, VPGNet, can detect and classify lanes and road markings, and predict a vanishing point with a single forward pass. Experimental results show that our approach achieves high accuracy and robustness under various conditions in realtime (20 fps). The benchmark and the VPGNet model will be publicly available In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions. We tackle rainy and low illumination conditions, which have not been extensively studied until now due to clear challenges. For example, images taken under rainy days are subject to low illumination, while wet roads cause light reflection and distort the appearance of lane and road markings. At night, color distortion occurs under limited illumination. As a result, no benchmark dataset exists and only a few developed algorithms work under poor weather conditions. To address this shortcoming, we build up a lane and road marking benchmark which consists of about 20,000 images with 17 lane and road marking classes under four different scenarios: no rain, rain, heavy rain, and night. We train and evaluate several versions of the proposed multi-task network and validate the importance of each task. The resulting approach, VPGNet, can detect and classify lanes and road markings, and predict a vanishing point with a single forward pass. Experimental results show that our approach achieves high accuracy and robustness under various conditions in real-time (20 fps). The benchmark and the VPGNet model will be publicly available. In this paper, a technique for the identification of the unwanted lane departure of a traveling vehicle on a road is proposed. A piecewise linear stretching function (PLSF) is used to improve the contrast level of the region of interest (ROI). Lane markings on the road are detected by dividing the ROI into two subregions and applying the Hough transform in each subregion independently. This segmentation approach improves the computational time required for lane detection. For lane departure identification, a distance-based departure measure is computed at each frame, and a necessary warning message is issued to the driver when such measure exceeds a threshold. The novelty of the proposed algorithm is the identification of the lane departure only using three lane-related parameters based on the Euclidean distance transform to estimate the departure measure. The use of the Euclidean distance transform in combination with the PLSF keeps the false alarm around 3 and the lane detection rate above 97 under various lighting conditions. Experimental results indicate that the proposed system can detect lane boundaries in the presence of several image artifacts, such as lighting changes, poor lane markings, and occlusions by a vehicle, and it issues an accurate lane departure warning in a short time interval. The proposed technique shows the efficiency with some real video sequences. In this paper, we introduce a robust lane detection method based on the combined convolutional neural network (CNN) with random sample consensus (RANSAC) algorithm. At first, we calculate edges in an image using a hat shape kernel and then detect lanes using the CNN combined with the RANSAC. If the road scene is simple, we can easily detect the lane by using the RANSAC algorithm only. But if the road scene is complex and includes roadside trees, fence, or intersection etc., then it is hard to detect lanes robustly because of noisy edges. To alleviate that problem, we use CNN in the lane detection before and after applying the RANSAC algorithm. In training process of CNN, input data consist of edge images in a region of interest (ROI) and target data become the images that have only drawn real white color lane in black background. The CNN structure consists of 8 layers with 3 convolutional layers, 2 subsampling layers and multi-layer perceptron (MLP) including 3 fully-connected layers. Convolutional and subsampling layers are hierarchically arranged and their arrangement represents a deep structure in deep learning. As a result, proposed lane detection algorithm successfully eliminates noise lines and the performance is found to be better than other formal line detection algorithms such as RANSAC and hough transform.