Lane Detection Image Processing Algorithm based on FPGA for Intelligent Vehicle

Zhan, Huiqin; Chen, Lin

doi:10.1109/cac48633.2019.8996283

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…Zhan and Chen [73] suggested a lane line detection technique based on image processing and deep learning based on the FPGA development platform to accomplish the fast lane line detection effect of structured roadways, with speeds up to 104 FPS. First, the camera captures road data, which is then transferred to the FPGA as image data via the AXI protocol.…”

Section: Ii) Deep Learning + Geometric Modellingmentioning

confidence: 99%

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Lane Detection in Autonomous Vehicles: A Systematic Review

et al. 2023

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One of the essential systems in autonomous vehicles for ensuring a secure circumstance for drivers and passengers is the Advanced Driver Assistance System (ADAS). Adaptive Cruise Control, Automatic Braking/Steer Away, Lane-Keeping System, Blind Spot Assist, Lane Departure Warning System, and Lane Detection are examples of ADAS. Lane detection displays information specific to the geometrical features of lane line structures to the vehicle's intelligent system to show the position of lane markings. This article reviews the methods employed for lane detection in an autonomous vehicle. A systematic literature review (SLR) has been carried out to analyze the most delicate approach to detecting the road lane for the benefit of the automation industry. One hundred and two publications from well-known databases were chosen for this review. The trend was discovered after thoroughly examining the selected articles on the method implemented for detecting the road lane from 2018 until 2021. The selected literature used various methods, with the input dataset being one of two types: self-collected or acquired from an online public dataset. In the meantime, the methodologies include geometric modeling and traditional methods, while AI includes deep learning and machine learning. The use of deep learning has been increasingly researched throughout the last four years. Some studies used stand-alone deep learning implementations for lane detection problems. Meanwhile, some research focuses on merging deep learning with other machine learning techniques and classical methodologies. Recent advancements imply that attention mechanism has become a popular combined strategy with deep learning methods. The use of deep algorithms in conjunction with other techniques showed promising outcomes. This research aims to provide a complete overview of the literature on lane detection methods, highlighting which approaches are currently being researched and the performance of existing state-of-the-art techniques. Also, the paper covered the equipment used to collect the dataset for the training process and the dataset used for network training, validation, and testing. This review yields a valuable foundation on lane detection techniques, challenges, and opportunities and supports new research works in this automation field. For further study, it is suggested to put more effort into accuracy improvement, increased speed performance, and more challenging work on various extreme conditions in detecting the road lane.

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Section: Ii) Deep Learning + Geometric Modellingmentioning

confidence: 99%

“…Next, Zhan and Chen [73] used a camera to acquire the data, which they then fed into an FPGA as image data using the AXI protocol. Next, the detection outcome for the real dataset collected from the author's autonomous vehicle was published in [15].…”

Section: ) Cameramentioning

confidence: 99%

Lane Detection in Autonomous Vehicles: A Systematic Review

et al. 2023

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“…The integration of this network serves as its primary objective, the improvement of the effectiveness of the network in demanding situations concerning the detection of the lane mark. The combination of DL with geometric modelling is one example of the integration of DL with another method [13,[28][29][30][31]. Another example is the integration of DL with ML, also integration of DL with DL which is known as the two serial DLs [32][33][34][35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

Fully Convolutional Network Model Applied Attention Mechanism on Kitti Lane Dataset for Lane Detection

Noor Jannah Zakaria,

Mohd Ibrahim Shapiai,

Rasli Abd Ghani

et al. 2024

ARASET

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This article investigated the attention mechanism implemented by the Fully Convolutional Network (FCN) Model on the Kitti Lane Dataset. Two attention mechanisms were applied in the deep learning model to improve traffic lane detection for autonomous vehicles. The Kitti lane dataset, which was generated in collaboration with Jannik Fritsch and Tobias Kuehl from Honda Research Europe GmbH, was selected for this study. The results demonstrate that the applied attention mechanism can effectively improve the network's feature representation on lane markings. Furthermore, this approach can improve the weighted information of lane line targets while decreasing irrelevant information. As a result, the proposed technique improved, obtaining more than 95% accuracy. Subsequently, the attention mechanism was implemented in the FCN model architecture to enhance the lane-detecting model. As a result, in the future, more comprehensive ideas, such as combining the FCN model with Transfer Learning, will play an essential part in investigating the improvement of lane detection areas.

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A Survey of FPGA-Based Vision Systems for Autonomous Cars

et al. 2022

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On the road to making self-driving cars a reality, academic and industrial researchers are working hard to continue to increase safety while meeting technical and regulatory constraints Understanding the surrounding environment is a fundamental task in self-driving cars. It requires combining complex computer vision algorithms. Although state-of-the-art algorithms achieve good accuracy, their implementations often require powerful computing platforms with high power consumption. In some cases, the processing speed does not meet real-time constraints. FPGA platforms are often used to implement a category of latency-critical algorithms that demand maximum performance and energy efficiency. Since self-driving car computer vision functions fall into this category, one could expect to see a wide adoption of FPGAs in autonomous cars. In this paper, we survey the computer vision FPGA-based works from the literature targeting automotive applications over the last decade. Based on the survey, we identify the strengths and weaknesses of FPGAs in this domain and future research opportunities and challenges.INDEX TERMS autonomous car, feature extraction, field programmable gate arrays, image classification, object detection, reconfigurable architectures Self-driving functions can be implemented in several computing platforms such as ASICs, CPUs, DSPs, GPUs, FPGAs, or in heterogeneous platforms composed by any

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Lane Detection Image Processing Algorithm based on FPGA for Intelligent Vehicle

Cited by 6 publications

References 6 publications

Lane Detection in Autonomous Vehicles: A Systematic Review

Lane Detection in Autonomous Vehicles: A Systematic Review

Fully Convolutional Network Model Applied Attention Mechanism on Kitti Lane Dataset for Lane Detection

A Survey of FPGA-Based Vision Systems for Autonomous Cars

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