Traffic Flow Parameters Collection under Variable Illumination Based on Data Fusion
Accurate traffic flow parameters are the supporting data for analyzing traffic flow characteristics. Vehicle detection using traffic surveillance pictures is a typical method for gathering traffic flow characteristics in urban traffic scenes. In complicated lighting conditions at night, however, nei...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2021/4592124 |
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| _version_ | 1832550880703938560 |
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| author | Shaojie Jin Ying Gao Shoucai Jing Fei Hui Xiangmo Zhao Jianzhen Liu |
| author_facet | Shaojie Jin Ying Gao Shoucai Jing Fei Hui Xiangmo Zhao Jianzhen Liu |
| author_sort | Shaojie Jin |
| collection | DOAJ |
| description | Accurate traffic flow parameters are the supporting data for analyzing traffic flow characteristics. Vehicle detection using traffic surveillance pictures is a typical method for gathering traffic flow characteristics in urban traffic scenes. In complicated lighting conditions at night, however, neither classical nor deep-learning-based image processing algorithms can provide adequate detection results. This study proposes a fusion technique combining millimeter-wave radar data with image data to compensate for the lack of image-based vehicle detection under complicated lighting to complete all-day parameters collection. The proposed method is based on an object detector named CenterNet. Taking this network as the cornerstone, we fused millimeter-wave radar data into it to improve the robustness of vehicle detection and reduce the time-consuming postcalculation of traffic flow parameters collection. We collected a new dataset to train the proposed method, which consists of 1000 natural daytime images and 1000 simulated nighttime images with a total of 23094 vehicles counted, where the simulated nighttime images are generated by a style translator named CycleGAN to reduce labeling workload. Another four datasets of 2400 images containing 20161 vehicles were collected to test the proposed method. The experimental results show that the method proposed has good adaptability and robustness at natural daytime and nighttime scenes. |
| format | Article |
| id | doaj-art-05069111bc364a4ba9adb3d0196d6d3b |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-05069111bc364a4ba9adb3d0196d6d3b2025-02-03T06:05:33ZengWileyJournal of Advanced Transportation0197-67292042-31952021-01-01202110.1155/2021/45921244592124Traffic Flow Parameters Collection under Variable Illumination Based on Data FusionShaojie Jin0Ying Gao1Shoucai Jing2Fei Hui3Xiangmo Zhao4Jianzhen Liu5School of Information and Engineering, Chang’an University, Xi’an, Shaanxi, ChinaSchool of Information and Engineering, Chang’an University, Xi’an, Shaanxi, ChinaSchool of Information and Engineering, Chang’an University, Xi’an, Shaanxi, ChinaSchool of Information and Engineering, Chang’an University, Xi’an, Shaanxi, ChinaSchool of Information and Engineering, Chang’an University, Xi’an, Shaanxi, ChinaJiaoke Transport Conultants LTD, Beijing, ChinaAccurate traffic flow parameters are the supporting data for analyzing traffic flow characteristics. Vehicle detection using traffic surveillance pictures is a typical method for gathering traffic flow characteristics in urban traffic scenes. In complicated lighting conditions at night, however, neither classical nor deep-learning-based image processing algorithms can provide adequate detection results. This study proposes a fusion technique combining millimeter-wave radar data with image data to compensate for the lack of image-based vehicle detection under complicated lighting to complete all-day parameters collection. The proposed method is based on an object detector named CenterNet. Taking this network as the cornerstone, we fused millimeter-wave radar data into it to improve the robustness of vehicle detection and reduce the time-consuming postcalculation of traffic flow parameters collection. We collected a new dataset to train the proposed method, which consists of 1000 natural daytime images and 1000 simulated nighttime images with a total of 23094 vehicles counted, where the simulated nighttime images are generated by a style translator named CycleGAN to reduce labeling workload. Another four datasets of 2400 images containing 20161 vehicles were collected to test the proposed method. The experimental results show that the method proposed has good adaptability and robustness at natural daytime and nighttime scenes.http://dx.doi.org/10.1155/2021/4592124 |
| spellingShingle | Shaojie Jin Ying Gao Shoucai Jing Fei Hui Xiangmo Zhao Jianzhen Liu Traffic Flow Parameters Collection under Variable Illumination Based on Data Fusion Journal of Advanced Transportation |
| title | Traffic Flow Parameters Collection under Variable Illumination Based on Data Fusion |
| title_full | Traffic Flow Parameters Collection under Variable Illumination Based on Data Fusion |
| title_fullStr | Traffic Flow Parameters Collection under Variable Illumination Based on Data Fusion |
| title_full_unstemmed | Traffic Flow Parameters Collection under Variable Illumination Based on Data Fusion |
| title_short | Traffic Flow Parameters Collection under Variable Illumination Based on Data Fusion |
| title_sort | traffic flow parameters collection under variable illumination based on data fusion |
| url | http://dx.doi.org/10.1155/2021/4592124 |
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