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      Analysis of the Influence of Foggy Weather Environment on the Detection Effect of Machine Vision Obstacles

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          Abstract

          This study is to analyze the influence of visibility in a foggy weather environment on the accuracy of machine vision obstacle detection in assisted driving. We present a foggy day imaging model and analyze the image characteristics, then we set up the faster region convolutional neural network (Faster R-CNN) as the basic network for target detection in the simulation experiment and use Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) data for network detection and classification training. PreScan software is used to build weather and traffic scenes based on a foggy imaging model, and we study object detection of machine vision in four types of weather condition—clear (no fog), light fog, medium fog, and heavy fog—by simulation experiment. The experimental results show that the detection recall is 91.55%, 85.21%, 72.54~64.79%, and 57.75% respectively in no fog, light fog, medium fog, and heavy fog environments. Then we used real scenes in medium fog and heavy fog environment to verify the simulation experiment. Through this study, we can determine the influence of bad weather on the detection results of machine vision, and hence we can improve the safety of assisted driving through further research.

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          Most cited references29

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          Very Deep Convolutional Networks for Large-Scale Image Recognition

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          In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.
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            Vision and the atmosphere

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              Review of Video and Image Defogging Algorithms and Related Studies on Image Restoration and Enhancement

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                08 January 2020
                January 2020
                : 20
                : 2
                : 349
                Affiliations
                Department of Transportation Engineering, College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China; heyongjiang1213@ 123456163.com (Y.H.); chaowang@ 123456sdust.edu.cn (C.W.); songrunze0623@ 123456163.com (R.S.)
                Author notes
                [* ]Correspondence: zhaohuiliu@ 123456sdust.edu.cn ; Tel.: +86-0532-87657875
                Author information
                https://orcid.org/0000-0001-6228-3016
                Article
                sensors-20-00349
                10.3390/s20020349
                7014178
                31936287
                c834b99b-ed3c-413b-861c-c2ca49505de3
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 21 November 2019
                : 06 January 2020
                Categories
                Article

                Biomedical engineering
                faster r-cnn,foggy environment,intelligent vehicles,machine vision,object recognition

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