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

Ruijia Lan; Yaohua Deng

doi:10.1117/12.2610999

14 October 2021 Design of multi-lane line detection algorithm based on semantic segmentation and clustering

Ruijia Lan, Yaohua Deng

Proceedings Volume 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation; 119301H (2021) https://doi.org/10.1117/12.2610999
Event: International Conference on Mechanical Engineering, Measurement Control, and Instrumentation (MEMCI 2021), 2021, Guangzhou, China

Abstract

In order to improve the detection accuracy and speed of lane detection, this paper proposes a multi-lane line detection algorithm based on the combination of semantic segmentation and clustering. First, we need to process the dataset, use a new semantic segmentation network to obtain lane instances, then perform binary segmentation on the image and embed a vector attribute to distinguish which lane the pixel belongs to, and then combine the two parts of data. Perform clustering and use polynomial fitting method to get the lane object on the image. After experimental testing, the detection speed of the algorithm reached 72fps, and the accuracy rate reached 96.5%, indicating that the algorithm has a high detection accuracy and speed.

Citation Download Citation

Ruijia Lan and Yaohua Deng "Design of multi-lane line detection algorithm based on semantic segmentation and clustering", Proc. SPIE 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation, 119301H (14 October 2021); https://doi.org/10.1117/12.2610999

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