A novel FPGA implementation of Hough Transform for straight lane detection

Hajjouji, I. El; Mars, S.; Asrih, Z.; Mourabit, Aimad El

doi:10.1016/j.jestch.2019.05.008

Cited by 34 publications

(18 citation statements)

References 16 publications

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“…Subsequently, all these candidates request further evaluation during postprocessing. In this stage, three assumptions can be employed: (a) the road lanes are assumed to be in the middle and lower regions of the image 12 ; (b) lanes parallel in the 3D real world cannot be horizontal or vertical in the 2D image plane 10 ; and (c) the road dividing lines have a strong contrast with the surroundings. With these assumptions, the false road lane candidates are finally eliminated.…”

Section: Lane Boundary Detection Theorymentioning

confidence: 99%

“…Consequently, the performance of road and lane perceptions, especially the lane detection technique, needs to be further improved. 10,11 Lane mark recognition is one of the most important parts of road understandings. 12 For example, a more reliable path planning approach for mobile object navigation can be achieved by the interaction of lane detection with obstacle avoidance.…”

Section: Introductionmentioning

confidence: 99%

“…Despite performing better in detecting weak lane appearance features, 22 such an approach is less efficient compared to edge-based solutions. 10 Machine learning (ML), especially deep learning (e.g. convolutional neural network), 23,24 is another technique that can be used in lane detection, 25,26 with much progress made in past 5 years.…”

Section: Introductionmentioning

confidence: 99%

“…30,31 The HT technique is such an algorithm and therefore being widely used when detecting straight road lanes. 10 Consequently, the research focus of this study is the HT-based algorithm. However, such an algorithm suffers from weak lane marks, noise, and occlusions.…”

Section: Introductionmentioning

confidence: 99%

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Practical limitations of lane detection algorithm based on Hough transform in challenging scenarios

Huang

Liu

2021

International Journal of Advanced Robotic Systems

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The vision-based road lane detection technique plays a key role in driver assistance system. While existing lane recognition algorithms demonstrated over 90% detection rate, the validation test was usually conducted on limited scenarios. Significant gaps still exist when applied in real-life autonomous driving. The goal of this article was to identify these gaps and to suggest research directions that can bridge them. The straight lane detection algorithm based on linear Hough transform (HT) was used in this study as an example to evaluate the possible perception issues under challenging scenarios, including various road types, different weather conditions and shades, changed lighting conditions, and so on. The study found that the HT-based algorithm presented an acceptable detection rate in simple backgrounds, such as driving on a highway or conditions showing distinguishable contrast between lane boundaries and their surroundings. However, it failed to recognize road dividing lines under varied lighting conditions. The failure was attributed to the binarization process failing to extract lane features before detections. In addition, the existing HT-based algorithm would be interfered by lane-like interferences, such as guardrails, railways, bikeways, utility poles, pedestrian sidewalks, buildings and so on. Overall, all these findings support the need for further improvements of current road lane detection algorithms to be robust against interference and illumination variations. Moreover, the widely used algorithm has the potential to raise the lane boundary detection rate if an appropriate search range restriction and illumination classification process is added.

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Section: Lane Boundary Detection Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Practical limitations of lane detection algorithm based on Hough transform in challenging scenarios

Huang

Liu

2021

International Journal of Advanced Robotic Systems

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“…At present, straight-line models are mostly used in lane detection algorithms to fit lane-lines. Considering that only color [12][13] or edge [14][15] features are extracted, the model is of low complexity, good real-time performance, and robustness to straight lane-lines. However, this method is limited to the inaccuracy of fitting results, i.e., lanelines can hardly be fitted in the form of curves in practical applications.…”

Section: Line Detectionmentioning

confidence: 99%

A Novel Lane Line Detection Based on Multi-feature Fusion and Windows Searching

Cai

Gao

Pan

et al. 2021

Preprint

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Lane detection embedded in intelligent vehicles can greatly improve the security of automatic driving. This work offers a new approach towards lane detection in the video in real-time combining multi-feature fusion and window searching. As the pre-procession, polygon filling is adopted to locate ROI (Region of Interest) in the video frames, which contain the lane-lines to be detected. To remove the backgrounds in the ROIs, we extract and fuse the features of color, histogram, and gradient of line lanes. Based on the density distribution of the pixels in the line lanes, the initial location is found by homography transformation. Then all candidate pixel points in the whole lane-line are extracted in the way of window searching. Finally, On the basis of the obtained lane-mark coordinates, a curve model is defined, and the model parameters are obtained by Least Square Estimation (LSE). Experimental results show the robustness and instantaneity of the proposed algorithm with the accuracy of 96% and the detecting time of only 20.7ms. In addition, lane-lines with misleading backdrops can also be detected such as yellow lane lines on the ground, shadow, bright light, lane-line defects and traffic light

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