The Visibility Measurement Based on Convolutional Neural Network

Liu, Tao; Li, Zechen; Mei, RuoWei; Lai, Can; Wang, HaiJiang; Hu, Shipeng

doi:10.1109/icmo49322.2019.9026141

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…In training, Relu were used as activation function introducing nonlinearity and Dropout layer were used to prevent the visibility detection training model from overfitting. Finally the accuracy of detection results based on this deep neural network model could reach 85% [8] .…”

Section: Combining Deep Learning and Image Detection Methodsmentioning

confidence: 91%

Review of atmospheric visibility detection methods

Zhao

Peng

et al. 2023

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

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Whether in land, sea or air transportation, atmospheric visibility is an important factor related to the safety of people's lives and property. How to continuously monitor low-visibility weather and achieve better results, so as to provide indicators for the traffic control department to make corresponding traffic control measures in a timely manner, is of great significance to the driving safety of the people. In recent years, more and more facts are proving that the problem of low recognition accuracy of traffic signs, traffic lights, roadblocks and lane lines in low-visibility environments limits the rapid development of technology for unmanned driving technology. Therefore, there is a growing need to discover methods that can detect visibility efficiently and inexpensively. This paper reviews and summarizes the traditional and emerging visibility detection methods in recent years, analyzes and briefly expounds the advantages and disadvantages of each method. This paper aims to give researchers easy access to new and old technologies in the field and to provide them with basic directions for innovative methodological techniques based on them.

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Section: Combining Deep Learning and Image Detection Methodsmentioning

confidence: 91%

Review of atmospheric visibility detection methods

Zhao

Peng

et al. 2023

International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023)

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“…where the input data A * and R * represent AIS data and radar data respectively, and the input data W * is the waterway information. Based on previous laboratory research results, visibility can be determined using a Convolutional Neural Network (CNN) and camera data [35]. Besides, our system can get more information.…”

Section: ) Radar Coordinate System Conversionmentioning

confidence: 99%

“…In the video detection process, according to the visibility identification model proposed in our previous research, the input images are first labeled with visibility level labels [35]. Subsequently, images with low and high visibility are input into target detection models with and without the dark channel prior [38], respectively.…”

Section: ) Radar Coordinate System Conversionmentioning

confidence: 99%

A Cyber-Physical Traffic Signaling System for Controlled Waterway in Inland River Based on Edge-centric IoT

Li,

Liang,

Gan

et al. 2023

Preprint

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“…This type of algorithm usually divides the visibility values into different levels, then uses the deep learning algorithm to extract the high-dimensional features of the image for end-to-end training and learning with the visibility level, and finally outputs the predicted visibility level. For example, VisNet was constructed by connecting three streams of deep integrated convolutional neural networks in parallel and was implemented to estimate visibility distances [15]; a convolution neural network visibility prediction model based on the Alex model was established [16]; visibility was detected and classified based on brightness, brightness saturation, intensity, variance, and other factors of the image and was divided into fog and non-fog images by using a convolution neural network [17]; and a deep learning method based on the ordinal relation and triplet relative learning was conducted to detect the visibility of foggy images [18]. However, these methods usually rely on a large dataset and quality of annotation, and there is still a significant difference between the output visibility level and the actual visibility value.…”

Section: Introductionmentioning

confidence: 99%

Video visibility prediction method for foggy weather based on spatio-temporal variation features

Li,

Dai,

Yao

et al. 2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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Visibility prediction affects travel safety and is a major concern for highway and airline authorities. However, current visibility prediction methods based on video data usually capture some features in static images, which are relatively simple and are not commonly used for continuous variation information of video data. This study proposes a visibility prediction method based on the spatio-temporal variation features of a video. Through the spatial division of the images from the video, the optimal spatial distribution of different features in the image is mapped using the correlation matrix, and the spatio-temporal variation information of the video is extracted based on the short-term stationary features of fog spatio-temporal evolution. Finally, the relationship between the spatiotemporal variation features of the video and visibility is constructed by combining the time-frequency localization features of the wavelet transform and the self learning ability of the neural network. Compared with the predicted results of traditional static image features, theR2, RMSE, and MAE of the visibility prediction results were improved by 0.1698 and reduced by 58.4142 and 20.0427, respectively. In addition, the R2, RMSE, and MAE of the prediction results based on wavelet neural networks can reach 0.9817, 28.3365, and 19.2098, respectively, compared to current mainstream prediction methods. This fully proves the scientificity and effectiveness of the method, which can be applied to the accurate monitoring of visibility in fog weather based on video data.

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The Visibility Measurement Based on Convolutional Neural Network

Cited by 7 publications

References 5 publications

Review of atmospheric visibility detection methods

Review of atmospheric visibility detection methods

A Cyber-Physical Traffic Signaling System for Controlled Waterway in Inland River Based on Edge-centric IoT

Video visibility prediction method for foggy weather based on spatio-temporal variation features

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