scholarly journals Lane Line Extraction in Raining Weather Images by Ridge Edge Detection with Improved MSR and Hessian Matrix

2021 ◽  
Vol 50 (4) ◽  
pp. 722-735
Author(s):  
W. Wang ◽  
F. Berholm ◽  
K. Hu ◽  
L. Zhao ◽  
S. Feng ◽  
...  
Keyword(s):  
Edge Detection ◽  
Road Traffic ◽  
Hessian Matrix ◽  
Local Area ◽  
Noise Removal ◽  
Detection Algorithm ◽  
Line Detection ◽  
Gap Filling ◽  
Long Gap ◽  
Lane Line

To accurately detect lane lines in road traffic images at raining weather, a edge detection based method is studied, which mainly includes four algorithms. (1) Firstly an image is enhanced by an improved Retinex algorithm; (2) Then, an algorithm based on the Hessian matrix is applied to strengthen lane lines; (3) To extract the feature points of a lane line, a ridge edge detection algorithm based on five line detection in four directions is proposed, in which, in light on the possible positions of lane lines in the image, it detects the maximum gray level points in the local area of the detecting point within the pre-set valid detection region; and (4) After the noise removal based on the minimum circumscribed rectangles, the candidate points of lane lines are connected as segments, and for the gap filling between segments, in order to make connection correctly, the algorithm makes the filling in two steps, short gap and long gap fillings, and the long gap filling is made on the combination of segment angle difference and gap distance and gap angle. By testing hundreds of images of the lane lines at raining weather and by comparing several traditional image enhancement and segmentation algorithms, the new method of the lane line detection can produce the satisfactory results.

scholarly journals Low Complexity Lane Detection Methods for Light Photometry System

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1665
Author(s):  
Jakub Suder ◽  
Kacper Podbucki ◽  
Tomasz Marciniak ◽  
Adam Dąbrowski
Keyword(s):  
Edge Detection ◽  
Optimal Path ◽  
Color Space ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Detection Methods ◽  
Raspberry Pi ◽  
Line Detection ◽  
Hsv Color Space

The aim of the paper was to analyze effective solutions for accurate lane detection on the roads. We focused on effective detection of airport runways and taxiways in order to drive a light-measurement trailer correctly. Three techniques for video-based line extracting were used for specific detection of environment conditions: (i) line detection using edge detection, Scharr mask and Hough transform, (ii) finding the optimal path using the hyperbola fitting line detection algorithm based on edge detection and (iii) detection of horizontal markings using image segmentation in the HSV color space. The developed solutions were tuned and tested with the use of embedded devices such as Raspberry Pi 4B or NVIDIA Jetson Nano.

A Novel Edge Detection Algorithm of Color Image

2013 ◽  
Vol 446-447 ◽  
pp. 976-980
Author(s):  
De Rui Song ◽  
Dao Yan Xu ◽  
Li Li
Keyword(s):  
Edge Detection ◽  
Impulse Noise ◽  
Color Image ◽  
Peer Group ◽  
Color Space ◽  
Noise Removal ◽  
Detection Algorithm ◽  
Image Edge Detection ◽  
Image Edge ◽  
Image Edges

This paper proposes a novel algorithm of edge detection using LUV color space. Firstly, according to peer group filtering (PGF), a nonlinear algorithm for image smoothing and impulse noise removal in color image is used. Secondly, color image edges in an image are obtained automatically by combining an improved isotropic edge detector and a fast entropy threshold technique. Thirdly, according to color distance between the pixel and its eight neighbor-pixels, color image edges can further be detected. Finally, the experiment demonstrates the outcome of proposed algorithm in color image edge detection.

Improved Lane Line Detection Algorithm Based on Hough Transform

2018 ◽  
Vol 28 (2) ◽  
pp. 254-260 ◽  
Author(s):  
Fang Zheng ◽  
Sheng Luo ◽  
Kang Song ◽  
Chang-Wei Yan ◽  
Mu-Chou Wang
Keyword(s):  
Hough Transform ◽  
Detection Algorithm ◽  
Line Detection ◽  
Lane Line

scholarly journals A Real-Time Lane Detection Algorithm Based on Intelligent CCD Parameters Regulation

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Ping-shu Ge ◽  
Lie Guo ◽  
Guo-kai Xu ◽  
Rong-hui Zhang ◽  
Tao Zhang
Keyword(s):  
Real Time ◽  
Road Traffic ◽  
Warning System ◽  
Detection Algorithm ◽  
Lane Detection ◽  
The Real ◽  
Lane Departure ◽  
Lane Line ◽  
Onboard System ◽  
Complexity Of Algorithm

Lane departure warning system (LDWS) has been regarded as an efficient method to lessen the damages of road traffic accident resulting from driver fatigue or inattention. Lane detection is one of the key techniques for LDWS. To overcome the contradiction between complexity of algorithm and the real-time requirement for vehicle onboard system, this paper introduces a new lane detection method based on intelligent CCD parameters regulation. In order to improve the real-time capability of the system, a CCD parameters regulating method is proposed which enhances the contrast between lane line and road surfaces and reduces image noise, so it lays a good foundation for the following lane detection. Hough transform algorithm is improved by selection and classification of seed points. Finally the lane line is extracted through some restrictions. Experimental results verify the effectiveness of the proposed method, which improves not only real-time capability but also the accuracy of the system.

scholarly journals Improvement of the Traditional Canny Edge Detection Algorithm by using Combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter

2019 ◽  
Vol 9 (2) ◽  
pp. 2392-2399
Keyword(s):  
Edge Detection ◽  
Noise Removal ◽  
Detection Algorithm ◽  
Arithmetic Mean ◽  
Harmonic Mean ◽  
Canny Edge Detection ◽  
Edge Detection Algorithm ◽  
Mean Filter ◽  
Canny Edge ◽  
Canny Edge Detection Algorithm

Canny Edge Detection Algorithm was very popular on the computer vision area which used to preserve the edges of the image. Due to the defect of the Canny Edge Detection Algorithm like no efficiency on noise removal, some improvement on the Canny Edge Detection Algorithm was done by the researchers. On this paper, a new enhanced Canny Edge Detection Algorithm will be propose which replaces the Gaussian Filter with combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter. The replace of Gaussian Filter with combination of Arithmetic Mean Filter, Harmonic Mean Filter and Geometric Mean Filter is to improve the performance of Canny Edge Detection Algorithm on noise removal. A comparison between Canny Edge Detection Algorithm proposed by this paper, Canny Edge Detection Algorithm proposed by (Ilkin, Tafralı, &Sahin, 2017) and traditional Canny Edge Detection Algorithm will be done. The comparison will done by using eight images with different type and size which corrupted by noise. The performance of three algorithms will be determined by using the Peak Signal to Noise Ratio (henceforth, PSNR) value which uses as a quantitative measure. From the result, the Canny Edge Detection proposed by this paper will provide a better performance on noise removal and which will give a better impact on preserve the edges of the images corrupted by noise.

scholarly journals Lane Line Detection System Based on Improved Yolo V3 Algorithm

2021 ◽  
Author(s):  
Gaoqing Ji ◽  
Yunchang Zheng
Keyword(s):  
Real Time ◽  
Detection System ◽  
Detection Algorithm ◽  
Horizontal Distribution ◽  
Lane Detection ◽  
Cluster Center ◽  
Line Detection ◽  
Detection Accuracy ◽  
Time Performance ◽  
Lane Line

Abstract Aiming at the problems of low accuracy and poor real-time performance of Yolo v3 algorithm in lane detection, a lane detection system based on improved Yolo v3 algorithm is proposed. Firstly, according to the characteristics of inconsistent vertical and horizontal distribution density of lane line pictures, the lane line pictures are divided into s * 2S grids; Secondly, the detection scale is adjusted to four detection scales, which is more suitable for small target detection such as lane line; Thirdly, the convolution layer in the original Yolo v3 algorithm is adjusted from 53 layers to 49 layers to simplify the network; Finally, the parameters such as cluster center distance and loss function are improved. The experimental results show that when using the improved detection algorithm for lane line detection, the average detection accuracy map value is 92.03% and the processing speed is 48 fps.Compared with the original Yolo v3 algorithm, it is significantly improved in detection accuracy and real-time performance.

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