Improved Lane Line Detection Algorithm Based on Hough Transform

Fang, Zheng; Luo, Sheng; Song, Kang; Yan, Changwei; Wang, Muchou

doi:10.1134/s1054661818020049

Cited by 76 publications

(26 citation statements)

References 8 publications

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“…Similarly, specific structural characteristics of lane, such as lane width 48 and lane shape, 50 can also be employed to extract lane boundaries. Zheng et al 51 and Luo et al 52 even proposed the lane detection algorithms assuming road dividing lines to be subject to multiple structural constraints (i.e. length constraint, parallel constraint, distribution constraint, pair constraint, and uniform width constraint).…”

Section: Optimization Directionsmentioning

confidence: 99%

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: Optimization Directionsmentioning

confidence: 99%

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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“…Compared with other algorithms, although the average processing time was relatively short, the average detection accuracy was the lowest and more likely to cause false or missed detection in actual road scenes. In Reference [50], the model-based lane detection method was adopted. Compared with literature [49], although the average detection accuracy did improved, the average processing time was too long.…”

Section: Lane Detectionmentioning

confidence: 99%

Lane Detection Algorithm for Intelligent Vehicles in Complex Road Conditions and Dynamic Environments

Cao

Song

et al. 2019

Sensors

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Lane detection is an important foundation in the development of intelligent vehicles. To address problems such as low detection accuracy of traditional methods and poor real-time performance of deep learning-based methodologies, a lane detection algorithm for intelligent vehicles in complex road conditions and dynamic environments was proposed. Firstly, converting the distorted image and using the superposition threshold algorithm for edge detection, an aerial view of the lane was obtained via region of interest extraction and inverse perspective transformation. Secondly, the random sample consensus algorithm was adopted to fit the curves of lane lines based on the third-order B-spline curve model, and fitting evaluation and curvature radius calculation were then carried out on the curve. Lastly, by using the road driving video under complex road conditions and the Tusimple dataset, simulation test experiments for lane detection algorithm were performed. The experimental results show that the average detection accuracy based on road driving video reached 98.49%, and the average processing time reached 21.5 ms. The average detection accuracy based on the Tusimple dataset reached 98.42%, and the average processing time reached 22.2 ms. Compared with traditional methods and deep learning-based methodologies, this lane detection algorithm had excellent accuracy and real-time performance, a high detection efficiency and a strong anti-interference ability. The accurate recognition rate and average processing time were significantly improved. The proposed algorithm is crucial in promoting the technological level of intelligent vehicle driving assistance and conducive to the further improvement of the driving safety of intelligent vehicles.

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“…Path pixel extraction can simply be regarded as an image operation of threshold segmentation, which divides the image into two regions: objects in the scene and the background, in terms of the difference of image threshold. The commonly used segmentation methods include the fixed single threshold, the fixed multiple threshold [20], the iterative optimal threshold, and the maximum inter-cluster variance (Otsu) [23]. Since under-or over-segmentation errors are unavoidable in the threshold segmentation for the current methods, path models have to be estimated according to a flock of target pixels with noise points.…”

Section: Vision Guidance Systemmentioning

confidence: 99%

“…If the path pixels are recognized correctly with few under-or over-segmentation errors, it is not difficult to obtain the path equation by means of model estimation methods. The points conforming to the parallel characteristics, the length and angle characteristics, and the intercept characteristics of lane lines are selected in Hough space and then directly converted into the lane line equation [20]. An effective lane detection method based on an improved Canny edge detector and least square fitting was proposed in [21].The dual-threshold selection of a traditional Canny detector was improved using the histogram concavity analysis, and the least square method was used to fit the feature points of detected edges to accurate and single-pixel wide lane.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace

Sun

Zou

et al. 2019

Applied Sciences

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Applying computer vision to mobile robot navigation has been studied more than twodecades. The most challenging problems for a vision-based AGV running in a complex workspaceinvolve the non-uniform illumination, sight-line occlusion or stripe damage, which inevitably resultin incomplete or deformed path images as well as many fake artifacts. Neither the fixed thresholdmethods nor the iterative optimal threshold methods can obtain a suitable threshold for the pathimages acquired on all conditions. It is still an open question to estimate the model parameters ofguide paths accurately by distinguishing the actual path pixels from the under- or oversegmentationerror points. Hence, an intelligent path recognition approach based on KPCA–BPNNand IPSO–BTGWP is proposed here, in order to resist the interferences from the complexworkspace. Firstly, curvilinear paths were recognized from their straight counterparts by means of apath classifier based on KPCA–BPNN. Secondly, an approximation method based on BTGWP wasdeveloped for replacing the curve with a series of piecewise lines (a polyline path). Thirdly, a robustpath estimation method based on IPSO was proposed to figure out the path parameters from a set ofpath pixels surrounded by noise points. Experimental results showed that our approach caneffectively improve the accuracy and reliability of a low-cost vision-guidance system for AGVs in acomplex workspace.

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Improved Lane Line Detection Algorithm Based on Hough Transform

Cited by 76 publications

References 8 publications

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

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

Lane Detection Algorithm for Intelligent Vehicles in Complex Road Conditions and Dynamic Environments

Intelligent Path Recognition against Image Noises for Vision Guidance of Automated Guided Vehicles in a Complex Workspace

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