Abstract
In order to adequately characterize the visual characteristics of atmospheric visibility and overcome the disadvantages of the traditional atmospheric visibility measurement method with significant dependence on preset reference objects, high cost, and complicated steps, this paper proposed an ensemble learning method for atmospheric visibility grading based on deep neural network and stochastic weight averaging. An experiment was conducted using the scene of an expressway, and three visibility levels were set, i.e., Level 1, Level 2, and Level 3. Firstly, the EfficientNet was transferred to extract the abstract features of the images. Then, training and grading were performed on the feature sets through the SoftMax regression model. Subsequently, the feature sets were ensembled using the method of stochastic weight averaging to obtain the atmospheric visibility grading model. The obtained datasets were input into the grading model and tested. The grading model classified the results into three categories, with the grading accuracy being 95.00%, 89.45%, and 90.91%, respectively, and the average accuracy of 91.79%. The results obtained by the proposed method were compared with those obtained by the existing methods, and the proposed method showed better performance than those of other methods. This method can be used to classify the atmospheric visibility of traffic and reduce the incidence of traffic accidents caused by atmospheric visibility.
Highlights
Atmospheric visibility is a critical item in road meteorological observation, which has an essential impact on traffic safety and human health
In order to verify the effectiveness of the proposed method based on the EfficientNet and stochastic weight averaging (SWA) algorithm, the cross-validation method was used to divide the training set and the validation set after obtaining the image dataset by the image acquisition device
A total of 2500 images with different visibility levels were used as the training set, and 500 images with different visibility levels were used as the verification set
Summary
Atmospheric visibility is a critical item in road meteorological observation, which has an essential impact on traffic safety and human health. Traditional visibility measurement methods include the visual inspection method, instrumental measurement method, and image-based grading method. Atmospheric visibility is calculated based on the image’s feature value, interest pane, optical contrast of the scene, or their combination, corrected by the histogram or other grading modules. Tang et al [6] revealed that learning could adequately reflect the visual features of visibility, and it could be used to solve the problem of the difficulty in constructing large-scale training datasets. You et al [8] proposed a deep learning method that estimated atmospheric visibility directly from outdoor images without relying on weather images or expensive instruments. Atmosphere 2021, 12, 869 of it is not so caring It is for these considerations that in subsequent articles, we are defining three levels of visibility instead of calculating specific values. A trained Softmax classifier is used in the image retrieval method to process each query image and feedback of its category
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
