CNN based lane detection with instance segmentation in edge-cloud computing

Wang, Wei; Lin, Hui; Wang, Junshu

doi:10.1186/s13677-020-00172-z

Cited by 35 publications

(14 citation statements)

References 39 publications

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“…They used distinctive methods like SCNN, VGG-FCN, U-NeT, Tradition, BINARY, AND EMBEDDING set of rules to gain excellent effects to detect lanes from image datasets [8]. Dataset 133235 pics 88880 images for the training set (66.7%) 9675 pictures for the validation set (7.3%) 34680 photographs for the check set (26.0%) to enhance SCNN for lane detection in harsh scenarios [9].…”

Section: Pertained Cnn-based Approachmentioning

confidence: 99%

Artificial Intelligence based Lane Detection using Satellite Images

Rehman¹,

Ghous²,

Malik

et al. 2023

ijisct

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Lane detection is one of the most important task for tragedy or emergency response using satellite imagery structures. Most of the researchers adopted Machine Learning (ML) approaches to detect white lines on the roads namely lanes. These approaches are more complex and computational. In order to identify knowledge gaps, this study compares and analyses the various lane detection and tracking algorithms that have been used in research papers in the past. Additionally, it provides an overview of some of the datasets used in various research papers. The main challenge in detecting the lane using satellite images is the weather, which makes it difficult to achieve the expected objectives.

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Section: Pertained Cnn-based Approachmentioning

confidence: 99%

Artificial Intelligence based Lane Detection using Satellite Images

Rehman¹,

Ghous²,

Malik

et al. 2023

ijisct

View full text Add to dashboard Cite

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“…Similarly, for overcoming lane detection in complex shadow and lighting conditions full of obstacles, a CNN-based method was presented by Wang et al [91]. From an inverse perspective, the application of a fixed transformation matrix generated errors as changes occurred, allowing the predicted exhaust point to infinitely shift upward or downward.…”

Section: Lane Detection and Trackingmentioning

confidence: 99%

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

2022

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In the past decade, autonomous vehicle systems (AVS) have advanced at an exponential rate, particularly due to improvements in artificial intelligence, which have had a significant impact on social as well as road safety and the future of transportation systems. However, the AVS is still far away from mass production because of the high cost of sensor fusion and a lack of combination of top-tier solutions to tackle uncertainty on roads. To reduce sensor dependency and to increase manufacturing along with enhancing research, deep learning-based approaches could be the best alternative for developing practical AVS. With this vision, in this systematic review paper, we broadly discussed the literature of deep learning for AVS from the past decade for real-life implementation in core fields. The systematic review on AVS implementing deep learning is categorized into several modules that cover activities including perception analysis (vehicle detection, traffic signs and light identification, pedestrian detection, lane and curve detection, road object localization, traffic scene analysis), decision making, end-to-end controlling and prediction, path and motion planning and augmented reality-based HUD, analyzing research works from 2011 to 2021 that focus on RGB camera vision. The literature is also analyzed for final representative outcomes as visualization in augmented reality-based head-up display (AR-HUD) with categories such as early warning, road markings for improved navigation and enhanced safety with overlapping on vehicles and pedestrians in extreme visual conditions to reduce collisions. The contribution of the literature review includes detailed analysis of current state-of-the-art deep learning methods that only rely on RGB camera vision rather than complex sensor fusion. It is expected to offer a pathway for the rapid development of cost-efficient and more secure practical autonomous vehicle systems.

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“…Yoo et al proposed a vanishing point estimation method based on a probabilistic voting procedure for detecting lanes in complex road environments [ 26 ]. Though these methods can detect lanes in good condition, their performance is unsatisfactory in various scenarios, including curved roads, rainy days, changing illumination conditions, blurry lane lines, and others [ 7 , 8 ].…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, these methods fail to deliver a satisfactory performance when dealing with difficult road curvature, blurred lane lines, complex road patterns, etc. [ 7 , 8 ]. Researchers also applied model-based methods such as dynamic programming [ 9 ], Support Vector Machine (SVM) [ 10 ], B-Snake model [ 11 ], etc.…”

Section: Introductionmentioning

confidence: 99%

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning

Khan

Haque

Hasan

et al. 2022

Sensors

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Lane detection plays a vital role in making the idea of the autonomous car a reality. Traditional lane detection methods need extensive hand-crafted features and post-processing techniques, which make the models specific feature-oriented, and susceptible to instability for the variations on road scenes. In recent years, Deep Learning (DL) models, especially Convolutional Neural Network (CNN) models have been proposed and utilized to perform pixel-level lane segmentation. However, most of the methods focus on achieving high accuracy while considering structured roads and good weather conditions and do not put emphasis on testing their models on defected roads, especially ones with blurry lane lines, no lane lines, and cracked pavements, which are predominant in the real world. Moreover, many of these CNN-based models have complex structures and require high-end systems to operate, which makes them quite unsuitable for being implemented in embedded devices. Considering these shortcomings, in this paper, we have introduced a novel CNN model named LLDNet based on an encoder–decoder architecture that is lightweight and has been tested in adverse weather as well as road conditions. A channel attention and spatial attention module are integrated into the designed architecture to refine the feature maps for achieving outstanding results with a lower number of parameters. We have used a hybrid dataset to train our model, which was created by combining two separate datasets, and have compared the model with a few state-of-the-art encoder–decoder architectures. Numerical results on the utilized dataset show that our model surpasses the compared methods in terms of dice coefficient, IoU, and the size of the models. Moreover, we carried out extensive experiments on the videos of different roads in Bangladesh. The visualization results exhibit that our model can detect the lanes accurately in both structured and defected roads and adverse weather conditions. Experimental results elicit that our designed method is capable of detecting lanes accurately and is ready for practical implementation.

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CNN based lane detection with instance segmentation in edge-cloud computing

Cited by 35 publications

References 39 publications

Artificial Intelligence based Lane Detection using Satellite Images

Artificial Intelligence based Lane Detection using Satellite Images

Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review

LLDNet: A Lightweight Lane Detection Approach for Autonomous Cars Using Deep Learning

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