Design of an embedded system for real-time lane detection based on the linear Hough transform

Josh Ralston; Hau Ngo

doi:10.1117/12.2588037

12 April 2021 Design of an embedded system for real-time lane detection based on the linear Hough transform

Josh Ralston, Hau Ngo

Proceedings Volume 11736, Real-Time Image Processing and Deep Learning 2021; 117360J (2021) https://doi.org/10.1117/12.2588037
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Modern Advanced Driver Assistance Systems (ADAS) require the ability to sense and process information in real-time. More specifically, these devices need to accurately and quickly detect lanes in images. The Hough transform (HT) is a very accurate method of finding lines in a still image. In order to meet real-time requirements and low power consumption, a proposed hardware architecture design for the Hough transform in a real-time lane detection system is presented. This design efficiently and aggressively utilizes the DSP and embedded memory blocks in a configurable platform to speed up the HT calculation as well as reduce the resource requirements of the system. The proposed design utilized a parallelpipeline architecture to allow for full area coverage of all line possibilities while optimizing for hardware restrictions. Initial results have shown that the proposed design achieve a normalized processing rate of 6.06 ns per pixel which is suitable for real-time lane detection application.

Conference Presentation

Citation Download Citation

Josh Ralston and Hau Ngo "Design of an embedded system for real-time lane detection based on the linear Hough transform", Proc. SPIE 11736, Real-Time Image Processing and Deep Learning 2021, 117360J (12 April 2021); https://doi.org/10.1117/12.2588037

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