Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A review

Chen, Weiwei; Wang, Weixing; Wang, Kevin; Li, Zhaoying; Li, Huan; Liu, Sheng

doi:10.1016/j.jtte.2020.10.002

Cited by 35 publications

(23 citation statements)

References 55 publications

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“…erefore, the logit model considering investor sentiment can achieve an early warning effect on the stock market (Zhang et al) [17]. Chen et al proposed a lane marking detection method based on machine vision and image processing, which realizes the early warning function of lane departure through traditional image processing and semantic segmentation, as an important guarantee of traffic safety (Chen et al) [18]. e analysis of the above research results shows that the early warning system plays an important role in maintaining social stability.…”

Section: Related Workmentioning

confidence: 99%

Early Warning of Real Estate Market Development Risk Based on Network News Topic Mining and Neural Network

Shen

2022

Mathematical Problems in Engineering

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With the rapid development of the information technology, mining news content efficiently is a difficult problem faced by the government and enterprises. The classification, clustering, and prediction of neural network are used in news mining. A keyword based on neural network and word vector are proposed as a text feature model, and the model is compared with other neural network models. This paper studies the vectorization of text for similarity recommendation and introduces two models: word vector model and text vector model doc2vec based on neural network. In the model with word vector as feature vector, the recommendation accuracy is about 75.35%, doc2vec model is about 44.5%, and the recommendation accuracy of the model with keyword as text is about 88.61%. The recommendation accuracy is about 91%, and the performance has greatly improved. The more the number of keywords, the better the recommendation effect of the model. When the number is about 20, the improvement of the recommendation effect tends to be gentle, and the continuous increase of the number of keywords will increase the operation time of the model. It is proposed that the keyword and word vector based on neural network together as the text vector model can more accurately mine news data, quickly obtain news information, and make prediction and early warning for many industries such as the real estate industry.

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Section: Related Workmentioning

confidence: 99%

Early Warning of Real Estate Market Development Risk Based on Network News Topic Mining and Neural Network

Shen

2022

Mathematical Problems in Engineering

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“…Image analyse is an important application in the field of artificial intelligence [ 37 ] and analysis of thermograms is within the range of image classification problems (as analysed in [ 38 ]). There are studies proving the effectiveness of convolutional neural networks in image processing (like [ 43 ]), or other approaches used in thermograph analysis like [ 38 , 44 , 45 ]). Thermograms are images with a quite specific structure based on a standard colour scale.…”

Section: Information System Supporting Thermographic Inspectionmentioning

confidence: 99%

Monitoring System for Railway Infrastructure Elements Based on Thermal Imaging Analysis

Stypułkowski

Gołda

Lewczuk

et al. 2021

Sensors

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The safety and reliability of railway transport requires new solutions for monitoring and quick identification of faults in the railway infrastructure. Electric heating devices (EORs) are the crucial element of turnouts. EORs ensure heating during low temperature periods when ice or snow can lock the turnout device. Thermal imaging is a response to the need for an EOR inspection tool. After processing, a thermogram is a great support for the manual inspection of an EOR, or the thermogram can be the input for a machine learning algorithm. In this article, the authors review the literature in terms of thermographic analysis and its applications for detecting railroad damage, analysing images through machine learning, and improving railway traffic safety. The EOR device, its components, and technical parameters are discussed, as well as inspection and maintenance requirements. On this base, the authors present the concept of using thermographic imaging to detect EOR failures and malfunctions using a practical example, as well as the concept of using machine learning mechanisms to automatically analyse thermograms. The authors show that the proposed method of analysis can be an effective tool for examining EOR status and that it can be included in the official EOR inspection calendar.

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“…To be specific, traditional vision-based lane detection algorithm mainly includes two steps: extraction of hand-crafted features and fitting geometrical curves. Researchers distinguish lanes from the prior characteristics including lane shape, color feature, and edge texture information [3]. en they use polynomial curves or splines to approach lane boundaries model with distinctive features.…”

Section: Introductionmentioning

confidence: 99%

Efficient Lane Detection Technique Based on Lightweight Attention Deep Neural Network

Yao

Chen

2022

Journal of Advanced Transportation

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For self-driving vehicles, detecting lane lines in changeable scenarios is a fundamental yet challenging task. The rise of deep learning in recent years has contributed to the thriving of autonomous driving. However, existing methods of lane detection based on deep learning have high requirements on computing environment, so their applicability is further restricted. This paper proposed an improved attention deep neural network (DNN), a lightweight semantic segmentation architecture catering for efficient computation in low memory, which contains two branches worked in different resolution. The proposed network integrates fine details captured by local interaction of pixels at high resolution into global contexts at low resolution, computing dense feature maps for prediction task. Based on the attributes of disparate feature resolution characteristics, different attention mechanisms are adopted to guide the network to effectively exploit the model parameters. The proposed network achieves comparable results with state-of-the-art methods on two popular lane detection benchmarks (TuSimple and CULane), with faster calculation efficiency at 259 frames-per-second (FPS) on CULane dataset, and the total number of model parameters only requires 1.57 M. This study provides a practical and meaningful reference for the application of lane detection in memory constrained devices.

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Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A review

Cited by 35 publications

References 55 publications

Early Warning of Real Estate Market Development Risk Based on Network News Topic Mining and Neural Network

Early Warning of Real Estate Market Development Risk Based on Network News Topic Mining and Neural Network

Monitoring System for Railway Infrastructure Elements Based on Thermal Imaging Analysis

Efficient Lane Detection Technique Based on Lightweight Attention Deep Neural Network

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