A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

Marzougui, Mehrez; Alasiry, Areej; Kortli, Yassin; Baili, Jamel

doi:10.1109/access.2020.2991930

Cited by 57 publications

(29 citation statements)

References 52 publications

(117 reference statements)

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“…Fourth, it is capable of detecting nearby structures to map robust relevant landmarks for highspeed railway mapping [18]. Lastly, it can be used to detect and track road lanes for lane departure warning systems [19].…”

Section: A Line-based Methodsmentioning

confidence: 99%

Detecting Power Lines Using Point Instance Network for Distribution Line Inspection

et al. 2021

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Power outages can disrupt daily domestic activities as well as the economy as operations are hampered when they occur. They can decrease work productivity by delaying operations that require electricity. The key solution to this problem is to ensure that there are fewer or no power interruptions. This can be achieved by ensuring secure and continuous network operations through regular maintenance and inspection. However, the traditional inspection technique of foot patrol is risky, laborious, and timeconsuming. A preferable contemporary technique uses an unmanned aerial vehicle (UAV) for inspecting distribution lines. Detecting power lines are crucial for real-time motion planning and navigation of UAVs. Previous techniques that depend on conventional filters and gradients may fail to detect power lines because of noisy backgrounds. Thus, this study proposes a novel technique by adopting the Transfer Learning approach The process involves fine-tuning a pretrained network from Point Instance Network (PINet) road lane detection model to fit the power line detection task. The proposed method extends the PINet model by adding a comparator for rotation block before it and a postprocessing block after it. This study generates four versions of the model, each of which was trained on one of the following datasets (i) self-gathered images captured by a handheld camera, (ii) a drone, (iii) publicly accessible images from the Power Line Dataset of Mountain Scene (PLDM), and (iv) Power Line Dataset of Urban Scene (PLDU). Experimental results on each dataset confirm the feasibility of the proposed approach.

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Section: A Line-based Methodsmentioning

confidence: 99%

Detecting Power Lines Using Point Instance Network for Distribution Line Inspection

et al. 2021

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“…Duan et al 39 even applied a trapezoid region as ROI, as shown in Figure 9(a). Considering a fixed size of ROI may not be appropriate for all conditions; both Wu et al 40 and Marzougui et al 41 employed an adaptive ROI approach. Similarly, with restrictions in the r-c plane, some studies limited the polar angle range in Hough space.…”

Section: Optimization Directionsmentioning

confidence: 99%

“…Again, such a range was also chosen personally based on their specific images. Zhou and Chen 43 argued the left boundary to be between [20 , 75 ] and the right one to be located in the range of [105 , 165 ], while Marzougui et al 41 chose [15 , 70 ] and [110 , 165 ] as the corresponding interest ranges. In the authors' viewpoint, the search range depends on vehicle pose and mounting location of cameras, and thus, various choices of the search range are reported.…”

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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“…The length estimation method adopts the Probabilistic Hough Transform method [29]. The standard Hough Transform essentially maps the image to its parameter space.…”

Section: Length Estimationmentioning

confidence: 99%

Research on Estimation Method of Geometric Features of Structured Negative Obstacle Based on Single-Frame 3D Laser Point Cloud

Zhi-ming

Chen

et al. 2021

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A single VLP-16 LiDAR estimation method based on a single-frame 3D laser point cloud is proposed to address the problem of estimating negative obstacles’ geometrical features in structured environments. Firstly, a distance measurement method is developed to determine the estimation range of the negative obstacle, which can be used to verify the accuracy of distance estimation. Secondly, the 3D point cloud of a negative obstacle is transformed into a 2D elevation raster image, making the detection and estimation of negative obstacles more intuitive and accurate. Thirdly, we compare the effects of a StatisticalOutlierRemoval filter, RadiusOutlier removal, and Conditional removal on 3D point clouds, and the effects of a Gauss filter, Median filter, and Aver filter on 2D image denoising, and design a flowchart for point cloud and image noise reduction and denoising. Finally, a geometrical feature estimation method is proposed based on the elevation raster image. The negative obstacle image in the raster is used as an auxiliary line, and the number of pixels is derived from the OpenCV-based Progressive Probabilistic Hough Transform to estimate the geometrical features of the negative obstacle based on the raster size. The experimental results show that the algorithm has high accuracy in estimating the geometric characteristics of negative obstacles on structured roads and has a practical application value for LiDAR environment perception research.

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A Lane Tracking Method Based on Progressive Probabilistic Hough Transform

Cited by 57 publications

References 52 publications

Detecting Power Lines Using Point Instance Network for Distribution Line Inspection

Detecting Power Lines Using Point Instance Network for Distribution Line Inspection

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

Research on Estimation Method of Geometric Features of Structured Negative Obstacle Based on Single-Frame 3D Laser Point Cloud

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