Analysis of the Influence of Foggy Weather Environment on the Detection Effect of Machine Vision Obstacles
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 (Fas...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/2/349 |
| _version_ | 1811306570463576064 |
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| author | Zhaohui Liu Yongjiang He Chao Wang Runze Song |
| author_facet | Zhaohui Liu Yongjiang He Chao Wang Runze Song |
| author_sort | Zhaohui Liu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T08:47:44Z |
| format | Article |
| id | doaj.art-652db47a24d04856a7242629f0207f4c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T08:47:44Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-652db47a24d04856a7242629f0207f4c2022-12-22T02:53:37ZengMDPI AGSensors1424-82202020-01-0120234910.3390/s20020349s20020349Analysis of the Influence of Foggy Weather Environment on the Detection Effect of Machine Vision ObstaclesZhaohui Liu0Yongjiang He1Chao Wang2Runze Song3Department of Transportation Engineering, College of Transportation, Shandong University of Science and Technology, Qingdao 266590, ChinaDepartment of Transportation Engineering, College of Transportation, Shandong University of Science and Technology, Qingdao 266590, ChinaDepartment of Transportation Engineering, College of Transportation, Shandong University of Science and Technology, Qingdao 266590, ChinaDepartment of Transportation Engineering, College of Transportation, Shandong University of Science and Technology, Qingdao 266590, ChinaThis 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.https://www.mdpi.com/1424-8220/20/2/349faster r-cnnfoggy environmentintelligent vehiclesmachine visionobject recognition |
| spellingShingle | Zhaohui Liu Yongjiang He Chao Wang Runze Song Analysis of the Influence of Foggy Weather Environment on the Detection Effect of Machine Vision Obstacles Sensors faster r-cnn foggy environment intelligent vehicles machine vision object recognition |
| title | Analysis of the Influence of Foggy Weather Environment on the Detection Effect of Machine Vision Obstacles |
| title_full | Analysis of the Influence of Foggy Weather Environment on the Detection Effect of Machine Vision Obstacles |
| title_fullStr | Analysis of the Influence of Foggy Weather Environment on the Detection Effect of Machine Vision Obstacles |
| title_full_unstemmed | Analysis of the Influence of Foggy Weather Environment on the Detection Effect of Machine Vision Obstacles |
| title_short | Analysis of the Influence of Foggy Weather Environment on the Detection Effect of Machine Vision Obstacles |
| title_sort | analysis of the influence of foggy weather environment on the detection effect of machine vision obstacles |
| topic | faster r-cnn foggy environment intelligent vehicles machine vision object recognition |
| url | https://www.mdpi.com/1424-8220/20/2/349 |
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