A novel algorithm of lane detection addressing varied scenarios of curved and dashed lanemarks
01 Nov 2015-pp 87-92
TL;DR: An efficient algorithm for detecting accurate lane inbounds under varying illumination and road conditions like curvy, straight and dashed lane markings, deterministically is proposed.
Abstract: Lane detection is a critical step in advanced driver assistance systems (ADAS). The detected lane information is used by later modules of warning and controlling the differential brake and steering angle. Here we propose an efficient algorithm for detecting accurate lane inbounds under varying illumination and road conditions like curvy, straight and dashed lane markings, deterministically. The current method uses edges extracted from frames and lane mask information extracted from spatial lane stripes through adaptive figure-ground segregation to determine the lane bounds. The proposed method has been extensively validated for varying real time scenarios against the state-of-art methods of lane detection.
Citations
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TL;DR: In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods, and a Computational experiment-based parallel lane detection framework is proposed.
Abstract: Lane detection is a fundamental aspect of most current advanced driver assistance systems U+0028 ADASs U+0029. A large number of existing results focus on the study of vision-based lane detection methods due to the extensive knowledge background and the low-cost of camera devices. In this paper, previous vision-based lane detection studies are reviewed in terms of three aspects, which are lane detection algorithms, integration, and evaluation methods. Next, considering the inevitable limitations that exist in the camera-based lane detection system, the system integration methodologies for constructing more robust detection systems are reviewed and analyzed. The integration methods are further divided into three levels, namely, algorithm, system, and sensor. Algorithm level combines different lane detection algorithms while system level integrates other object detection systems to comprehensively detect lane positions. Sensor level uses multi-modal sensors to build a robust lane recognition system. In view of the complexity of evaluating the detection system, and the lack of common evaluation procedure and uniform metrics in past studies, the existing evaluation methods and metrics are analyzed and classified to propose a better evaluation of the lane detection system. Next, a comparison of representative studies is performed. Finally, a discussion on the limitations of current lane detection systems and the future developing trends toward an Artificial Society, Computational experiment-based parallel lane detection framework is proposed.
144 citations
Cites methods from "A novel algorithm of lane detection..."
...Suddamalla et al detected the curves and straight lanes using pixel intensity and edge information with lane markings being extracted with adaptive threshold techniques [10]....
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TL;DR: This work proposes a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor.
Abstract: Recently, autonomous vehicles, particularly self-driving cars, have received significant attention owing to rapid advancements in sensor and computation technologies. In addition to traffic sign recognition, road lane detection is one of the most important factors used in lane departure warning systems and autonomous vehicles for maintaining the safety of semi-autonomous and fully autonomous systems. Unlike traffic signs, road lanes are easily damaged by both internal and external factors such as road quality, occlusion (traffic on the road), weather conditions, and illumination (shadows from objects such as cars, trees, and buildings). Obtaining clear road lane markings for recognition processing is a difficult challenge. Therefore, we propose a method to overcome various illumination problems, particularly severe shadows, by using fuzzy system and line segment detector algorithms to obtain better results for detecting road lanes by a visible light camera sensor. Experimental results from three open databases, Caltech dataset, Santiago Lanes dataset (SLD), and Road Marking dataset, showed that our method outperformed conventional lane detection methods.
36 citations
TL;DR: A vision-based lane detection system with dynamic integration and online evaluation, which works robustly in various complex situations (e.g. shadows, night, and lane missing scenarios) with a monocular camera.
Abstract: Lane detection techniques have been widely studied in the last two decades and applied in many advance driver assistance systems However, the development of a robust lane detection system, which can deal with various road conditions and efficiently evaluate its detection results in real time, is still of great challenge In this study, a vision-based lane detection system with dynamic integration and online evaluation is proposed To increase the robustness of the lane detection system, the integration system dynamically processes two lane detection modules First, a primary lane detection module is designed based on the steerable filter and Hough transform algorithm Then, a secondary algorithm, which combines the Gaussian mixture model for image segmentation and random sample consensus for lane model fitting, will be activated when the primary algorithm encounters a low detection confidence To detect the colour and line style of the ego lanes and evaluate the lane detection system in real time, a lane sampling and voting technique is proposed By combining the sampling and voting system system with prior lane geometry knowledge, the evaluation system can efficiently recognise the false detections The system works robustly in various complex situations (eg shadows, night, and lane missing scenarios) with a monocular camera
30 citations
01 Nov 2018
TL;DR: A detection loss function is proposed to train the fully convolutional lane detection network whose output is pixel-wise detection of lane categories and location and results show that the classification accuracy of the classification network model is larger than 97.5%.
Abstract: Numerous groups have conducted many studies on traffic lane detection. However, most methods detect lane regions by color feature or shape models designed by human. In this paper, a traffic lane detection method using fully convolutional neural network is proposed. To extract the suitable lane feature, a small neural network is built to implement feature extraction from large amount of images. The parameters of lane classification network model are utilized to initialize layers' parameters in lane detection network. In particular, a detection loss function is proposed to train the fully convolutional lane detection network whose output is pixel-wise detection of lane categories and location. The designed detection loss function consists of lane classification loss and regression loss. With detected lane pixels, lane marking can be easily realized by random sample consensus rather than complex post-processing. Experimental results show that the classification accuracy of the classification network model for each category is larger than 97.5%. And detection accuracy of the model trained by proposed detection loss function can reach 82.24% in 29 different road scenes.
19 citations
Cites background from "A novel algorithm of lane detection..."
...Some researchers also exploit the spatial information with mask [13][14] to realize lane detection....
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TL;DR: A novel binarization algorithm based on min-between-max thresholding (MBMT) is proposed that addresses the issue of outlier rejection in an efficient way and handles the effect of shadow, illumination variation, and other factors in time-sliced images in an automated manner.
Abstract: Automotive imaging is a recent trend in research to assist drivers and is finally moving forward to achieve the goal of designing a driverless car. Along with a state-of-the-art algorithm, a state-of-the-art validation framework is also a requirement to ensure the quality of the system. This paper proposes an enhancement of the ground truth determination for automated lane detection system. The approach of time slicing has been built up on the binary framework. However, the classical binarization algorithms are not found to be good enough to address the particular domain of lane detection in an unconstrained environment and varied scenarios of lane structures, including curvy and dashed lane marks. This paper proposes a novel binarization algorithm based on min-between-max thresholding (MBMT). The adaptive binarization addresses the issue of outlier rejection in an efficient way and handles the effect of shadow, illumination variation, and other factors in time-sliced images in an automated manner. Additionally, this paper identifies the limitation of the classical time-slice-based approach even with time MBMT for ground truth determination and addresses the same through the second level of adaptation by spatial MBMT. Finally, a complete mathematical model is presented to validate any arbitrary lane detection algorithm with respect to the ground truth determined through the said method of hybrid or modified MBMT or $\text{M}^{2}\text{BMT}$ .
13 citations
References
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TL;DR: A robust algorithm, called CHEVP, is presented for providing a good initial position for the B-Snake model, and a minimum error method by Minimum Mean Square Error (MMSE) is proposed to determine the control points of the B -Snake model by the overall image forces on two sides of lane.
812 citations
"A novel algorithm of lane detection..." refers background or methods in this paper
...Next, our algorithm flow and the experimental results have been presented and compared against the state-ofart in section VI. Finally the findings are concluded in section VII with possible directions towards future works....
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...The lane markings may vary in color (white and yellow for India, USA, Europe; Blue for South Korea), width and shape (solid and dashed)....
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20 Jun 2011
TL;DR: This work defines a knee formally for continuous functions using the mathematical concept of curvature and compares its definition against alternatives, and evaluates Kneedle's accuracy against existing algorithms on both synthetic and real data sets and its performance in two different applications.
Abstract: Computer systems often reach a point at which the relative cost to increase some tunable parameter is no longer worth the corresponding performance benefit. These ``knees'' typically represent beneficial points that system designers have long selected to best balance inherent trade-offs. While prior work largely uses ad hoc, system-specific approaches to detect knees, we present Kneedle, a general approach to on line and off line knee detection that is applicable to a wide range of systems. We define a knee formally for continuous functions using the mathematical concept of curvature and compare our definition against alternatives. We then evaluate Kneedle's accuracy against existing algorithms on both synthetic and real data sets, and evaluate its performance in two different applications.
689 citations
TL;DR: A pixel-hierarchy feature descriptor is proposed to model the contextual information shared by lane markings with the surrounding road region and a robust boosting algorithm to select relevant contextual features for detecting lane markings is proposed.
Abstract: Road scene analysis is a challenging problem that has applications in autonomous navigation of vehicles. An integral component of this system is the robust detection and tracking of lane markings. It is a hard problem primarily due to large appearance variations in lane markings caused by factors such as occlusion (traffic on the road), shadows (from objects like trees), and changing lighting conditions of the scene (transition from day to night). In this paper, we address these issues through a learning-based approach using visual inputs from a camera mounted in front of a vehicle. We propose the following: 1) a pixel-hierarchy feature descriptor to model the contextual information shared by lane markings with the surrounding road region; 2) a robust boosting algorithm to select relevant contextual features for detecting lane markings; and 3) particle filters to track the lane markings, without knowledge of vehicle speed, by assuming the lane markings to be static through the video sequence and then learning the possible road scene variations from the statistics of tracked model parameters. We investigate the effectiveness of our algorithm on challenging daylight and night-time road video sequences.
169 citations
"A novel algorithm of lane detection..." refers methods in this paper
...Moreover, the methods generally approximates/ estimates the location of the lane....
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Book•
22 Apr 2015
TL;DR: This text reviews the field of digital image processing from the different perspectives offered by the separate domains of signal processing and pattern recognition, representing the latest trends in industry and academic research.
Abstract: This text reviews the field of digital image processing from the different perspectives offered by the separate domains of signal processing and pattern recognition. The book describes a rich array of applications, representing the latest trends in industry and academic research. To inspire further interest in the field, a selection of worked-out numerical problems is also included in the text. The content is presented in an accessible manner, examining each topic in depth without assuming any prior knowledge from the reader, and providing additional background material in the appendices. Features: covers image enhancement techniques in the spatial domain, the frequency domain, and the wavelet domain; reviews compression methods and formats for encoding images; discusses morphology-based image processing; investigates the modeling of object recognition in the human visual system; provides supplementary material, including MATLAB and C++ code, and interactive GUI-based modules, at an associated website.
21 citations
"A novel algorithm of lane detection..." refers methods in this paper
...Next, our algorithm flow and the experimental results have been presented and compared against the state-ofart in section VI. Finally the findings are concluded in section VII with possible directions towards future works....
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18 Feb 2013
TL;DR: This paper presents a much advanced and efficient lane detection algorithm based on (ROI) Region of Interest segmentation, which can detect the lane markings accurately and quickly.
Abstract: This paper presents a much advanced and efficient lane detection algorithm. The algorithm is based on (ROI) Region of Interest segmentation. In this algorithm images are pre-processed by a top-hat transform for de-noising and enhancing contrast. ROI of a test image is then extracted. For detecting lines in the ROI, Hough transform is used. Estimation of the distance between Hough origin and lane-line midpoint is made. Lane departure decision is made based on the difference between these distances. As for the simulation part we have used Matlab software.Experiments show that the proposed algorithm can detect the lane markings accurately and quickly.
16 citations
"A novel algorithm of lane detection..." refers background in this paper
...Wang et al. [2] proposed dividing the image region into sub regions and fitting Hough lines to the edges in each sub regions and therefore determining the vanishing point....
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