Robust Nighttime Road Lane Line Detection using Bilateral Filter and SAGC under Challenging Conditions

Sultana, Samia; Ahmed, Boshir

doi:10.1109/iccrd51685.2021.9386516

Cited by 12 publications

(8 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, authors of [12,13] developed an effective procedure for road lanes detection in low light situations and utilized visible spectrum imaging sensors.…”

Section: Related Workmentioning

confidence: 99%

Sharpening and Detection of Road Studs for Intelligent Vehicles at Nighttime

Ṣiddiqi

Alrashdi

2022

Security and Communication Networks

View full text Add to dashboard Cite

Intelligent vehicles need to survive against various environments on roads such as sunlit, unclear, showery, shadowy, and inside tunnel conditions. This research designs a robust approach for detecting road studs at nighttime, which is the combination of various statistical methods. This detection approach is a unique approach developed for the detection of road lanes instead of road-painted lanes. Therefore, we detect road studs (cat eyes) instead of the painted lanes on roads at nighttime as the road studs have higher intensities at nighttime. First, we utilized Butterworth low-pass filter in order to sharpen the images. Second, we converted the image to grayscale and extracted the corresponding region of interest (ROI) from it. Then, the Canny edge detection algorithm was applied to create boundary lines in images. Finally, the Hough transform was applied to detect the desired lanes in the images, which are the road studs, and hence we successfully detected the road studs in images. We have used our own dataset for the stud’s detection, which considered most of the limitations of the previous datasets. Also, the dataset was collected in naturalistic environments at nighttime. The experimental result presents that the designed approach is accurate and robust for road stud detection against nighttime.

show abstract

“…On the other hand, authors of [12,13] developed an effective procedure for road lanes detection in low light situations and utilized visible spectrum imaging sensors.…”

Section: Related Workmentioning

confidence: 99%

Sharpening and Detection of Road Studs for Intelligent Vehicles at Nighttime

Ṣiddiqi

Alrashdi

2022

Security and Communication Networks

View full text Add to dashboard Cite

show abstract

“…in which, ||⋅|| 1 represents L 1 norm, p i,j is the prediction for the j-th row space point. With the addition of the classification loss, the total loss constraint L total is, L total = L cls + 𝛼L sim + 𝛽L lc (6) in which, L lc is the segmentation loss, L cls is classification loss, α is set to 0.5, and β is set to 0.8. Softmax is used to improve the segmentation model to learn discriminative features and classification ability.…”

Section: Loss Function Based On Row-selectionmentioning

confidence: 99%

“…For complex road conditions, such as blur, light, even complete obstruction, curves and intersections, traditional image processing methods were difficult to achieve lane line detection [1]. Therefore, some researchers tried to introduce deep learning methods into lane line detection [2][3][4][5][6][7][8][9][10][11]. Deep learning has gradually become a mainstream method in the lane detection field.…”

Section: Introductionmentioning

confidence: 99%

Lightweight lane line detection based on learnable cluster segmentation with self‐attention mechanism

Qin

et al. 2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

Pixel segmentation is one of the most commonly used deep learning methods for modern lane line detection. Although deep segmentation outperforms traditional methods, there are two main problems: slow speed and limited receptive field. In response to these problems, this paper proposes a lightweight lane line detection algorithm based on learnable cluster segmentation and self‐attention mechanism, which has extremely fast speed and the ability to adapt to real scenes. The lane detection process is considered as clustering under row segmentation. The data is processed through row segmentation and fed into a self‐attention mechanism. In addition to the benchmark dataset for lane detection, the algorithm was ported to real vehicles for real‐time road testing. Two tests show that our method performs very well on TuSimple, with an accuracy of 97.15%, an F1 score of 73.5 on CULane, and a speed of 142.7 frames per second (FPS), which solves the problem of slow cluster segmentation, while improving the accuracy of row segmentation. In the new scenario, the method has a misjudgment rate of only 6.7% for lane line points, which is suitable for the high standard requirements of autonomous driving.

show abstract

“…For a long time, many institutions have been striving to develop an effective lane detection system [8]. Grey scaling reduces a three-channel (Red, Green, Blue) image to a singlechannel (monochromatic shades from black to white) image, with each pixel containing only the RGB image's intensity information [9]. To detect the left and right lane markings on the road, a powerful road lane marker identification method is needed.…”

Section: Related Workmentioning

confidence: 99%

“…We used element-based strategies for the path finding investigation in this study. Reduced permeability owing to inclement weather, line disconnection, lack of clarity in path markings, shadows, brightening, and light reflection, and complex street-based rules are all substantial obstacles to path discovery and calculation [9]. Various component-based approaches for path recognition and stamping have been offered to various academics [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Robust and real-time multi-lane and single lane detection in Indian highway scenarios

Hanuman¹,

Kumar²

2021

E3S Web Conf.

View full text Add to dashboard Cite

In the Advanced Driver Assistance System (ADAS), lane detection plays a vital role to avoid road accidents of an Autonomous vehicle. Also, autonomous vehicles should be able to navigate by themselves, in-order to do, it needs to understand its surrounding conditions like a human. So that vehicle can determine its path in streets and highways it can maintain lane manoeuvre. Also, It has become the most fundamental aspect to consider in current ADAS research. One of the major hurdles in self-driving vehicle research is identifying the curved lanes, multiple lanes with challenging light, and weather conditions, especially in Indian highway scenarios. As it is a vision-based lane detection approach we are using OpenCV library which consists of multiple algorithms like the optimization of canny edge detection to find out the edges, features of the lane and Hough Transform for lane line generation and apply on the particular region of interest.

show abstract

Robust Nighttime Road Lane Line Detection using Bilateral Filter and SAGC under Challenging Conditions

Cited by 12 publications

References 18 publications

Sharpening and Detection of Road Studs for Intelligent Vehicles at Nighttime

Sharpening and Detection of Road Studs for Intelligent Vehicles at Nighttime

Lightweight lane line detection based on learnable cluster segmentation with self‐attention mechanism

Robust and real-time multi-lane and single lane detection in Indian highway scenarios

Contact Info

Product

Resources

About