Design of multi-lane line detection algorithm based on semantic segmentation and clustering

2021 ◽  
Author(s):  
Ruijia Lan ◽  
Yaohua Deng
Keyword(s):  
Semantic Segmentation ◽  
Detection Algorithm ◽  
Line Detection ◽  
Lane Line

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 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 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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