Lane detection is the most basic part of automatic driving, driver assistance and violation-detection systems. A lane detection method based on semantic discrimination is proposed to address the problems of diversity in illumination, occlusion and lack of prior knowledge about lane markings. Two symmetric ridge operators are designed to improve the precision of candidate-pixel extraction. The double-constrained random sample consensus method (DC-RANSAC) introduces colour and geometric constraints into the lane fitting to reduce the number of iterations and improve the accuracy of the hypothetical model. Classifiers are added to further validate the hypothetical model and identify its underlying semantics. The proposed method was evaluated using two different data sets with various scenarios, including unclear lane markings, dense traffic, occlusion of vehicles, complex shadows, road surface markings, poor lighting conditions, and unknown number of lane markings. The detailed evaluations show that the detection rate of the proposed method is comparable with that of existing state-of-the-art lane detection methods, whereas the precision rate is much higher. Moreover, the experiments prove the reliability of the proposed algorithm in lane marking semantic recognition.
Robust multi-lane detection method based on semantic discrimination
IET Intelligent Transport Systems ; 14 , 9 ; 1142-1152
2020-07-30
11 pages
Article (Journal)
Electronic Resource
English
object detection , detection rate , image classification , double-constrained random sample consensus method , semantic discrimination , classifiers , hypothetical model , violation-detection systems , road surface markings , automatic driving , robust multilane detection method , driver assistance , unclear lane markings , candidate-pixel extraction , lane detection methods , road traffic , symmetric ridge operators , feature extraction , basic part , lane detection method , semantic recognition , driver information systems , image colour analysis
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