Lane Departure detection using image processing techniques

Baili, Jamel; Marzougui, Mehrez; Sboui, Ameur; Lahouar, Samer; Hergli, Mounir; Bose, J. Subash Chandra; Besbes, Kamel

doi:10.1109/anti-cybercrime.2017.7905298

Cited by 19 publications

(9 citation statements)

References 4 publications

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“…The image contrast enhancement issue in computerized pictures can be drawn nearer from different philosophies, among which is numerical morphology [12]. Such administrators comprise in agreement to some closeness basis, in choosing for each purpose of the investigated picture, another dark level between two examples [13]. Despite the fact that morphological difference has been generally considered, there are no approaches, from the perspective prepared to do all the while normalizing and improving the balance in pictures with poor lighting [14] [15].…”

Section: Fig 1 Image To Blocks Divisionmentioning

confidence: 99%

“…This strategy depends on the deterioration of a picture into various sub-groups of morphology followed by homomorphic sifting and afterward calculation dependent on differential development is utilized to decide best increase and organizing segments for each sub-band. K. Grabowski et al [13] build up a method utilizing edge-discovery. The places of the edges are recognized first utilizing inclination based administrators, at that point morphological sifting is applied.…”

Section: Literature Surveymentioning

confidence: 99%

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Morphological Transformation In Poor Lighting Images For Image Contrast Enhancement

Rao¹

2021

TURCOMAT

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The paper presents a novel algorithm for the computation of the image decomposition using a morphological filter with reconstruction. The target applications are image contrast enhancement especially those with high dynamic content. Both bright and dark regions contrast enhancement were considered. A new hardware efficient implementation of decomposition is presented. Following decomposition in 5 levels of detail a local contrast enhancement is performed. The new reconstruction algorithm and its hardware implementation as proposed is shown to be independent on structural element size and that it results in a predictable time frame operation. A mixed schematic and VHDL/Verilog description of the decomposition filters was synthesized and results show far higher speed performance compared with solutions found in recent literature. Image organizing strategies are essentially required to the pictures captured by satellites. The grey levels in the image captured by satellites need to be normalized for improving picture contrast. The pictures captured by satellites are generally poor in quality and the contrast levels are also very less. In this manuscript a differentiation approach dependent on scientific morphology called rotational morphological transformations are proposed using Modified Opening by Reconstruction Method (MORM). This methodology upgrade pictures with poor contrast and unable to recognize the objects in the image. In the images with poor lightening, morphological operations are performed to improve its clarity and contrast. Picture enhancement has been done by applying morphological operations on the satellite images considered. The technique utilizes data from picture by squares, while the morphological methods change the strategy using the contrast enhancement activity, which is utilized to characterize the multi-foundation poor lightening pictures. The total picture handling procedure is implemented utilizing MATLAB reproduction model. The MORM method is compared with the traditional methods and the results show that the proposed method is better in improving the accuracy rate.

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Section: Fig 1 Image To Blocks Divisionmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Morphological Transformation In Poor Lighting Images For Image Contrast Enhancement

Rao¹

2021

TURCOMAT

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“…HT detects all the straight lines passing through the feature point and stores the results in the accumulator matrix. Lane boundary lines positions are obtained by looking for the local maximum value in the accumulator matrix [30]. HT transforms the straight line model in Eq.…”

Section: Feature Extractionmentioning

confidence: 99%

Lane Detection Based on Block-wise Hough Based Features and ANN

Wai¹,

Yu²

2021

IJIES

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Effective and automated lane marking detection system is one of the important part of many intelligent transportation systems. Many researches of lane detection techniques have been conducted on academic and automobile industry and applied in intelligent transportation systems. However, It is still a challenging problem because of the various environmental conditions such as shadows, lane occlusion and illumination invariance that can decrease the lane detection performance. To deal with these issues, a reliable lane marking detection system that use the block-wise Hough based features is proposed. We propose the Region of interest extraction that divides the image into sub-image blocks to detect the lane line and block-wise Hough Transform feature extraction approach to extract the lane line features. Artificial Neural Network (ANN) applys the extracted features to detect the lane. The efficiency and accuracy of our system is evaluated by using the Caltech lane dataset. The average correct detection rate is 94.67% and the computational time is 17.64 ms/frame by applying the proposed method.

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“…For example, Nima et al [6] covert RGB color space into YCbCr space and HSV space, and implement lane detection by the gradient detection operator. Jamel Baili et al [7] use a horizontal difference filter to simplify the edge detection process and group the detected edge points into a straight line by the improved Hough transformation. Qian Kidd et al [8] utilize the Sobel edge detection operator to detect lane edge information, and detect possible lane line by the modified Hough transform on the region of interest.…”

Section: Introductionmentioning

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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Lane Departure detection using image processing techniques

Cited by 19 publications

References 4 publications

Morphological Transformation In Poor Lighting Images For Image Contrast Enhancement

Morphological Transformation In Poor Lighting Images For Image Contrast Enhancement

Lane Detection Based on Block-wise Hough Based Features and ANN

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

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