Detection of lane line based on Robert operator

For overcoming the shortage of Otsu method, proposed an improved Otsu threshold segmentation algorithm. On the basis of Otsu threshold segmentation algorithm, the gray level was divided into two classes according to the image segmentation, to determine the best threshold by comparing their center distance, so as to achieve peak line recognition under the condition of multiple gray levels. Then did experiments on image segmentation of the lane line with MATLAB by traditional Otsu threshold segmentation algorithm and the improved algorithm, the threshold of traditional Otsu threshold segmentation algorithm is 144 and the threshold of the improved Otsu threshold segmentation algorithm is 131, the processing time is within 0.453 s. Test results show that the white part markings appear more, the intersection place of white lines and the background is more clear, so this method can identify lane markings well and meet the real-time requirements.

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Detection method for auto guide vehicle’s walking deviation based on image thinning and Hough transform

Measurement and Control ◽

10.1177/0020294019833073 ◽

2019 ◽

Vol 52 (3-4) ◽

pp. 252-261 ◽

Cited By ~ 1

Author(s):

Xiaohua Cao ◽

Daofan Liu ◽

Xiaoyu Ren

Keyword(s):

Hough Transform ◽

Detection Method ◽

Detection Algorithm ◽

Visual Navigation ◽

Guide Line ◽

Actual Environment ◽

Lane Line ◽

Image Thinning ◽

High Level ◽

Refinement Algorithm

Auto guide vehicle’s position deviation always appears in its walking process. Current edge approaches applied in the visual navigation field are difficult to meet the high-level requirements of complex environment in factories since they are easy to be affected by noise, which results in low measurement accuracy and unsteadiness. In order to avoid the defects of edge detection algorithm, an improved detection method based on image thinning and Hough transform is proposed to solve the problem of auto guide vehicle’s walking deviation. First, the image of lane line is preprocessed with gray processing, threshold segmentation, and mathematical morphology, and then, the refinement algorithm is employed to obtain the skeleton of the lane line, combined with Hough detection and line fitting, the equation of the guide line is generated, and finally, the value of auto guide vehicle’s walking deviation can be calculated. The experimental results show that the methodology we proposed can deal with non-ideal factors of the actual environment such as bright area, path breaks, and clutters on road, and extract the parameters of the guide line effectively, after which the value of auto guide vehicle’s walking deviation is obtained. This method is proved to be feasible for auto guide vehicle in indoor environment for visual navigation.

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Fast Drivable Areas Estimation with Multi-Task Learning for Real-Time Autonomous Driving Assistant

Applied Sciences ◽

10.3390/app112210713 ◽

2021 ◽

Vol 11 (22) ◽

pp. 10713

Author(s):

Dong-Gyu Lee

Keyword(s):

Real Time ◽

Computational Efficiency ◽

Autonomous Driving ◽

Learning Approaches ◽

Practical Applications ◽

Task Learning ◽

Lane Line ◽

High Level ◽

Time Autonomous

Autonomous driving is a safety-critical application that requires a high-level understanding of computer vision with real-time inference. In this study, we focus on the computational efficiency of an important factor by improving the running time and performing multiple tasks simultaneously for practical applications. We propose a fast and accurate multi-task learning-based architecture for joint segmentation of drivable area, lane line, and classification of the scene. An encoder-decoder architecture efficiently handles input frames through shared representation. A comprehensive understanding of the driving environment is improved by generalization and regularization from different tasks. The proposed method learns end-to-end through multi-task learning on a very challenging Berkeley Deep Drive dataset and shows its robustness for three tasks in autonomous driving. Experimental results show that the proposed method outperforms other multi-task learning approaches in both speed and accuracy. The computational efficiency of the method was over 93.81 fps at inference, enabling execution in real-time.

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