A Much Advanced and Efficient Lane Detection Algorithm for Intelligent Highway Safety

Sharma, Sachin; Shah, D. J.

doi:10.5121/csit.2013.3106

Cited by 13 publications

(3 citation statements)

References 9 publications

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“…numerous sensing modalities like stereo sight, LIDAR, GPS, and has been used for higher improvement in understanding lanes A top-view transformation model has been planned for a vehicle parking assistant [3]. Here the Region of Interest (ROI) segmentation technique has been applied for intelligent road safety [4]. During this algorithmic program a top-hat transformation is being created as a preprocessing step for enhancing distinction of the image.…”

Section: Literature Surveymentioning

confidence: 99%

Lane Detection and Lane Departure Warning System

2021

IJATCSE

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Lane detection is important for autonomous vehicles. For this reason, many approaches use lane boundary information to locate the vehicle inside the street, or to integrate GPS-based localization. Advanced driverassistance systems are developed to assist drivers in the driving process reducing road accidents. In this work, we present an end-to-end system for lane identification, clustering and classification, based on two cascaded neural networks, that runs in real-time. The first step is camera calibration which is used to remove the effect of lens distortion. Then a canny edge detection algorithm finds the edges of the images. Then the region of interest (ROI) is selected. The ROI is actually based on the rectangular shape appearing at the bottom of the image. ROI removes the unwanted region in the image. The potential lane markers are then determined using the Hough transform to analyze lane boundaries. Once the lane pixels are found, these pixels are continuously scanned to obtain the best linear regression analysis.It is qualified to be applied on highways and urban roadways. It also has been successfully verified in sunny, and rainy conditions for both day and night.

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Section: Literature Surveymentioning

confidence: 99%

Lane Detection and Lane Departure Warning System

2021

IJATCSE

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“…For improving the efficient of performance, lower area of a lane image is usually considered as region of interest (ROI) [4]. And this region is further divided into left and right sub-regions by supposing the width of lane is fixed [1,9]. Segmenting ROI will reduce the complexity of lane detection.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, it is necessary to set lower area of lane image as ROI.In the lower part, road lane are present. The lower view seen by a camera which can be situated inside a car near rear view mirror[9]. This region will be segmented by OTSU, and the foreground lane will be divided from the surrounding environment.Foreground object segmentation by OTSU.…”

mentioning

confidence: 99%

Algorithm of Lane Detection on an Appropriate Limited Region

Zhang

et al. 2013

AMM

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This paper proposed a lane detection algorithm for urban environment. The algorithm was concerned on selecting an appropriate limited region of interest (ROI) by OTSU segmentation. Then candidates of lane markers were extracted by Canny, finally the lane boundaries were detected by Hough transform. The limited ROI helps to identification lane in an appropriate region. This process have the effect of enhancement in the speed of operation. The proposed algorithm was simulated in MATLAB. The test databases were shared by Fondazione Bruno Kessler (FBK). The experiments show that lane boundaries can be detected correctly although they are fade. Feature-based method is usually affected by intension of image. Several characteristics of roads need to be considered further for detection more precisely.

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The analysis of lane detection algorithms using histogram shapes and Hough transform

Ketcham

Ganokratanaa

2015

International Journal of Intelligent Computing and Cybernetics

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Purpose – The purpose of this paper is to develop a lane detection analysis algorithm by Hough transform and histogram shapes, which can effectively detect the lane markers in various lane road conditions, in driving system for drivers. Design/methodology/approach – Step 1: receiving image: the developed system is able to acquire images from video files. Step 2: splitting image: the system analyzes the splitting process of video file. Step 3: cropping image: specifying the area of interest using crop tool. Step 4: image enhancement: the system conducts the frame to convert RGB color image into grayscale image. Step 5: converting grayscale image to binary image. Step 6: segmenting and removing objects: using the opening morphological operations. Step 7: defining the analyzed area within the image using the Hough transform. Step 8: computing Houghline transform: the system operates the defined segment to analyze the Houghline transform. Findings – This paper presents the useful solution for lane detection by analyzing histogram shapes and Hough transform algorithms through digital image processing. The method has tested on video sequences filmed by using a webcam camera to record the road as a video file in a form of avi. The experimental results show the combination of two algorithms to compare the similarities and differences between histogram and Hough transform algorithm for better lane detection results. The performance of the Hough transform is better than the histogram shapes. Originality/value – This paper proposed two algorithms by comparing the similarities and differences between histogram shapes and Hough transform algorithm. The concept of this paper is to analyze between algorithms, provide a process of lane detection and search for the algorithm that has the better lane detection results.

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A Much Advanced and Efficient Lane Detection Algorithm for Intelligent Highway Safety

Cited by 13 publications

References 9 publications

Lane Detection and Lane Departure Warning System

Lane Detection and Lane Departure Warning System

Algorithm of Lane Detection on an Appropriate Limited Region

The analysis of lane detection algorithms using histogram shapes and Hough transform

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