• Title/Summary/Keyword: Road Lane Detection

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Lane Marking Detection of Mobile Robot with Single Laser Rangefinder (레이저 거리 센서만을 이용한 자율 주행 모바일 로봇의 도로 위 정보 획득)

  • Jung, Byung-Jin;Park, Jun-Hyung;Kim, Taek-Young;Kim, Deuk-Young;Moon, Hyung-Pil
    • Journal of Institute of Control, Robotics and Systems
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    • v.17 no.6
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    • pp.521-525
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    • 2011
  • Lane marking detection is one of important issues in the field of autonomous mobile robot. Especially, in urban environment, like pavement roads of downtown or tour tracks of Science Park, which have continuous patterns on the surface of the road, the lane marking detection becomes more important ability. Although there were many researches about lane detection and lane tracing, many of them used vision sensors mainly to detect lane marking. In this paper, we obtain 2 dimensional library data of 'Intensity' and 'Distance' using one laser rangefinder only. We design a simple classifier and filtering algorithm for the lane detection which uses only one LRF (Laser Range Finder). Allowing extended usage of LRF, this research provides more functionality not only in range finding but also in lane detecting to mobile robots. This work will be technically helpful for robot developers to design more simple and efficient autonomous driving system using LRF.

Lane Detection Algorithm for Night-time Digital Image Based on Distribution Feature of Boundary Pixels

  • You, Feng;Zhang, Ronghui;Zhong, Lingshu;Wang, Haiwei;Xu, Jianmin
    • Journal of the Optical Society of Korea
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    • v.17 no.2
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    • pp.188-199
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    • 2013
  • This paper presents a novel algorithm for nighttime detection of the lane markers painted on a road at night. First of all, the proposed algorithm uses neighborhood average filtering, 8-directional Sobel operator and thresholding segmentation based on OTSU's to handle raw lane images taken from a digital CCD camera. Secondly, combining intensity map and gradient map, we analyze the distribution features of pixels on boundaries of lanes in the nighttime and construct 4 feature sets for these points, which are helpful to supply with sufficient data related to lane boundaries to detect lane markers much more robustly. Then, the searching method in multiple directions- horizontal, vertical and diagonal directions, is conducted to eliminate the noise points on lane boundaries. Adapted Hough transformation is utilized to obtain the feature parameters related to the lane edge. The proposed algorithm can not only significantly improve detection performance for the lane marker, but it requires less computational power. Finally, the algorithm is proved to be reliable and robust in lane detection in a nighttime scenario.

Sharpness-aware Evaluation Methodology for Haze-removal Processing in Automotive Systems

  • Hwang, Seokha;Lee, Youngjoo
    • IEIE Transactions on Smart Processing and Computing
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    • v.5 no.6
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    • pp.390-394
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    • 2016
  • This paper presents a new comparison method for haze-removal algorithms in next-generation automotive systems. Compared to previous peak signal-to-noise ratio-based comparisons, which measure similarity, the proposed modulation transfer function-based method checks sharpness to select a more suitable haze-removal algorithm for lane detection. Among the practical filtering schemes used for a haze-removal algorithm, experimental results show that Gaussian filtering effectively preserves the sharpness of road images, enhancing lane detection accuracy.

Advanced Lane Detecting Algorithm for Unmanned Vehicle

  • Moon, Hee-Chang;Lee, Woon-Sung;Kim, Jung-Ha
    • 제어로봇시스템학회:학술대회논문집
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    • 2003.10a
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    • pp.1130-1133
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    • 2003
  • The goal of this research is developing advanced lane detecting algorithm for unmanned vehicle. Previous lane detecting method to bring on error become of the lane loss and noise. Therefore, new algorithm developed to get exact information of lane. This algorithm can be used to AGV(Autonomous Guide Vehicle) and LSWS(Lane Departure Warning System), ACC(Adapted Cruise Control). We used 1/10 scale RC car to embody developed algorithm. A CCD camera is installed on top of vehicle. Images are transmitted to a main computer though wireless video transmitter. A main computer finds information of lane in road image. And it calculates control value of vehicle and transmit these to vehicle. This algorithm can detect in input image marked by 256 gray levels to get exact information of lane. To find the driving direction of vehicle, it search line equation by curve fitting of detected pixel. Finally, author used median filtering method to removal of noise and used characteristic part of road image for advanced of processing time.

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Efficient Lane Detection Using Histogram Based Segmentation (히스토그램을 이용한 효율적인 차선검출)

  • 남기환;배철수
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.7 no.5
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    • pp.1062-1067
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    • 2003
  • A vision system for Intelligent vehicles here. The system exploits the characteristics of the gray level histogram of the road to detect lane markers. Each lane maker is then analyzed using a decision tree, and finally the relations between lane markers are analyzed to create structures defining the lane boundaries. The resulting system also generates images that can be used ae preprocessing stages in lane detection, lane tracking or obstacle detection algorithm. The system runs in realtime ay rates of about 30Hz.

A Study on Image Processing Algorithms for Improving Lane Detectability at Night Based on Camera (카메라 기반 야간 차선 인식율 개선을 위한 영상처리 알고리즘에 대한 연구)

  • Kim, Heungryong;Lee, Seonbong
    • Transactions of the Korean Society of Automotive Engineers
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    • v.21 no.1
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    • pp.51-60
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    • 2013
  • In this paper, to control the existing headlamp control system using steering wheel angle more efficiently and more actively, image processing algorithm which improved the detection rate of lane at night based on camera was suggested. And to recognize road lane more clearly in the conditions of low illumination, new algorithms were developed in the aspects of improving brightness, extracting clear lane edge and using the characteristics of lane. Through this research, it turned out that lane detection ability by using the normalized stretching, angular mask and expected-area scan have good performance in the night compare to existing algorithms.

Lane and Obstacle Recognition Using Artificial Neural Network (신경망을 이용한 차선과 장애물 인식에 관한 연구)

  • Kim, Myung-Soo;Yang, Sung-Hoon;Lee, Sang-Ho;Lee, Suk
    • Journal of the Korean Society for Precision Engineering
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    • v.16 no.10
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    • pp.25-34
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    • 1999
  • In this paper, an algorithm is presented to recognize lane and obstacles based on highway road image. The road images obtained by a video camera undergoes a pre-processing that includes filtering, edge detection, and identification of lanes. After this pre-processing, a part of image is grouped into 27 sub-windows and fed into a three-layer feed-forward neural network. The neural network is trained to indicate the road direction and the presence of absence of an obstacle. The proposed algorithm has been tested with the images different from the training images, and demonstrated its efficacy for recognizing lane and obstacles. Based on the test results, it can be said that the algorithm successfully combines the traditional image processing and the neural network principles towards a simpler and more efficient driver warning of assistance system

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Lane Spline Generation Using Edge Detection Robust to Environmental Changes (외부 환경 변화에 강인한 에지 검출을 통한 차선의 스플라인 생성)

  • Kwon, Bo-Chul;Shin, Dongwon
    • Journal of Broadcast Engineering
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    • v.17 no.6
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    • pp.1069-1079
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    • 2012
  • Lane detection with the use of a camera is an essential task required for the development of advanced driving assistance system. In this paper, edges of the lane are generated by applying Canny's method. The edge detection usually makes different results for several environmental conditions depending on the clearness of lane quality, so that it sometimes causes wrong lane detection. Therefore, we propose robust algorithm to environmental changes that automatically adjusts parameter for edge detection and generates edges more stably. Based on the acquired edges, we finally generate the spline curve of lane by using Catmull Rom spline.

Lane Detection System using CNN (CNN을 사용한 차선검출 시스템)

  • Kim, Jihun;Lee, Daesik;Lee, Minho
    • IEMEK Journal of Embedded Systems and Applications
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    • v.11 no.3
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    • pp.163-171
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    • 2016
  • Lane detection is a widely researched topic. Although simple road detection is easily achieved by previous methods, lane detection becomes very difficult in several complex cases involving noisy edges. To address this, we use a Convolution neural network (CNN) for image enhancement. CNN is a deep learning method that has been very successfully applied in object detection and recognition. In this paper, we introduce a robust lane detection method based on a CNN combined with random sample consensus (RANSAC) algorithm. Initially, we calculate edges in an image using a hat shaped kernel, then we detect lanes using the CNN combined with the RANSAC. In the training process of the CNN, input data consists of edge images and target data is images that have real white color lanes on an otherwise black background. The CNN structure consists of 8 layers with 3 convolutional layers, 2 subsampling layers and multi-layer perceptron (MLP) of 3 fully-connected layers. Convolutional and subsampling layers are hierarchically arranged to form a deep structure. Our proposed lane detection algorithm successfully eliminates noise lines and was found to perform better than other formal line detection algorithms such as RANSAC

The Detection of the Lane Curve using the Lane Model on the Image Coordinate Systems (이미지 좌표계상의 차선 모델을 이용한 차선 휨 검출)

  • 박종웅;이준웅;장경영;정지화;고광철
    • Transactions of the Korean Society of Automotive Engineers
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    • v.11 no.1
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    • pp.193-200
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    • 2003
  • This paper proposes a novel algorithm to recognize the curve of a structured road. The proposed algorithm uses an LCF (Lane Curve Function) obtained by the transformation of a parabolic function defined on world coordinate into image coordinate. Unlike other existing methods, the algorithm needs no transformation between world coordinate and image coordinate owing to the LCF. In order to search for an LCF describing the lane best, the differential comparison between the slope of an assumed LCF and the phase angle of edge pixels in the LROI (Lane Region Of Interest) constructed by the LCF is implemented. As finding the true LCF, the lane curve is determined. The proposed method is proved to be efficient through various kinds of images, providing the reliable curve direction and the valid curvature compared to the real road.