Deep Learning-Based Method for Detecting Traffic Flow Parameters Under Snowfall
In recent years, advancements in computer vision have yielded new prospects for intelligent transportation applications, specifically in the realm of automated traffic flow data collection. Within this emerging trend, the ability to swiftly and accurately detect vehicles and extract traffic flow par...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/10/12/301 |
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| author | Cheng Jian Tiancheng Xie Xiaojian Hu Jian Lu |
| author_facet | Cheng Jian Tiancheng Xie Xiaojian Hu Jian Lu |
| author_sort | Cheng Jian |
| collection | DOAJ |
| description | In recent years, advancements in computer vision have yielded new prospects for intelligent transportation applications, specifically in the realm of automated traffic flow data collection. Within this emerging trend, the ability to swiftly and accurately detect vehicles and extract traffic flow parameters from videos captured during snowfall conditions has become imperative for numerous future applications. This paper proposes a new analytical framework designed to extract traffic flow parameters from traffic flow videos recorded under snowfall conditions. The framework encompasses four distinct stages aimed at addressing the challenges posed by image degradation and the diminished accuracy of traffic flow parameter recognition caused by snowfall. The initial two stages propose a deep learning network for removing snow particles and snow streaks, resulting in an 8.6% enhancement in vehicle recognition accuracy after snow removal, specifically under moderate snow conditions. Additionally, the operation speed is significantly enhanced. Subsequently, the latter two stages encompass yolov5-based vehicle recognition and the employment of the virtual coil method for traffic flow parameter estimation. Following rigorous testing, the accuracy of traffic flow parameter estimation reaches 97.2% under moderate snow conditions. |
| format | Article |
| id | doaj-art-3c1d298377ad40e6bc473ffb3fc63c45 |
| institution | DOAJ |
| issn | 2313-433X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj-art-3c1d298377ad40e6bc473ffb3fc63c452025-08-20T02:53:30ZengMDPI AGJournal of Imaging2313-433X2024-11-01101230110.3390/jimaging10120301Deep Learning-Based Method for Detecting Traffic Flow Parameters Under SnowfallCheng Jian0Tiancheng Xie1Xiaojian Hu2Jian Lu3Nanjing LES Information Technology Co., Ltd., Nanjing 211189, ChinaJiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, ChinaJiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, ChinaJiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 211189, ChinaIn recent years, advancements in computer vision have yielded new prospects for intelligent transportation applications, specifically in the realm of automated traffic flow data collection. Within this emerging trend, the ability to swiftly and accurately detect vehicles and extract traffic flow parameters from videos captured during snowfall conditions has become imperative for numerous future applications. This paper proposes a new analytical framework designed to extract traffic flow parameters from traffic flow videos recorded under snowfall conditions. The framework encompasses four distinct stages aimed at addressing the challenges posed by image degradation and the diminished accuracy of traffic flow parameter recognition caused by snowfall. The initial two stages propose a deep learning network for removing snow particles and snow streaks, resulting in an 8.6% enhancement in vehicle recognition accuracy after snow removal, specifically under moderate snow conditions. Additionally, the operation speed is significantly enhanced. Subsequently, the latter two stages encompass yolov5-based vehicle recognition and the employment of the virtual coil method for traffic flow parameter estimation. Following rigorous testing, the accuracy of traffic flow parameter estimation reaches 97.2% under moderate snow conditions.https://www.mdpi.com/2313-433X/10/12/301snow removaldeep learning networkvirtual coiltraffic flow parameter estimationvehicle detection |
| spellingShingle | Cheng Jian Tiancheng Xie Xiaojian Hu Jian Lu Deep Learning-Based Method for Detecting Traffic Flow Parameters Under Snowfall Journal of Imaging snow removal deep learning network virtual coil traffic flow parameter estimation vehicle detection |
| title | Deep Learning-Based Method for Detecting Traffic Flow Parameters Under Snowfall |
| title_full | Deep Learning-Based Method for Detecting Traffic Flow Parameters Under Snowfall |
| title_fullStr | Deep Learning-Based Method for Detecting Traffic Flow Parameters Under Snowfall |
| title_full_unstemmed | Deep Learning-Based Method for Detecting Traffic Flow Parameters Under Snowfall |
| title_short | Deep Learning-Based Method for Detecting Traffic Flow Parameters Under Snowfall |
| title_sort | deep learning based method for detecting traffic flow parameters under snowfall |
| topic | snow removal deep learning network virtual coil traffic flow parameter estimation vehicle detection |
| url | https://www.mdpi.com/2313-433X/10/12/301 |
| work_keys_str_mv | AT chengjian deeplearningbasedmethodfordetectingtrafficflowparametersundersnowfall AT tianchengxie deeplearningbasedmethodfordetectingtrafficflowparametersundersnowfall AT xiaojianhu deeplearningbasedmethodfordetectingtrafficflowparametersundersnowfall AT jianlu deeplearningbasedmethodfordetectingtrafficflowparametersundersnowfall |