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杨蓉,杨晓虎,玉雄侯. 辅助驾驶系统中浓雾天识别方法分析[J]. 科学技术与工程, 2021, 21(24): 10387-10392.
Yang Rong,Yang Xiaohu,Yu Xionghou.Analysis of Recognition Method of Dense Fog in Assistant Driving System[J].Science Technology and Engineering,2021,21(24):10387-10392.
辅助驾驶系统中浓雾天识别方法分析
Analysis of Recognition Method of Dense Fog in Assistant Driving System
投稿时间:2020-11-23  修订日期:2021-05-23
DOI:
中文关键词:  雾天识别  辅助驾驶系统  卷积神经网络  胶囊网络
英文关键词:fog identification  assistant driving system  convolutional neural network  capsule network
基金项目:国家自然科学(61703116);广西科技基地和人才专项项目(2018AD19349);广西创新驱动发展专项(AA18242045-3);广西自然科学(2017GXNSFBA198228);广西教育厅科研项目(2018KY0024);南宁市科技局重点研发计划项目(20192065)。
        
作者单位
杨蓉 广西大学机械工程学院
杨晓虎 广西大学机械工程学院
玉雄侯 广西大学机械工程学院
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中文摘要:
      辅助驾驶系统中通过计算能见度信息对雾天进行识别的方法存在一定的局限性。针对此问题,本文利用机器学习方法设计了可用于辅助驾驶系统的浓雾天识别算法,避免能见度测试方法的局限性。研究首先建立了基于驾驶场景的图像训练集。然后基于卷积神经网络和胶囊网络分别设计CNN-DFR10和CN-DFR5浓雾天识别算法模型,算法模型通过设置概率阈值的方法对天气类型进行区分,不同的概率值范围对应不同的天气类型。最后通过对比分析CNN-DFR10和CN-DFR5在浓雾天、雨天、阴天和晴朗天四种天气类型中的测试结果,发现CNN-DFR10算法对天气的识别准确率为86.9%,CN-DFR5算法的识别准确率为97.5%,后一种算法比前者能够更有效地从四种天气类型图像中区分出浓雾天和非浓雾天。
英文摘要:
      In assisted driving systems, the method of using visibility information to identify foggy days has certain limitations. Aiming at this problem, the machine learning method is used to design a dense fog recognition algorithm that can be used in assisted driving systems, avoided the limitations of the visibility method. Firstly, an image training set based on driving scene was established. Then CNN-DFR10 and CN-DFR5 algorithm models was designed based on the convolution neural network and capsule network, respectively. The algorithm models distinguished weather types by setting probability thresholds, and different probability value ranges corresponded to different weather types. Finally, by comparing and analyzing the test results of CNN-DFR10 and CN-DFR5 in the four weather types of dense fog, rain, cloudy and sunny, it is found that the accuracy of the CNN-DFR10 algorithm for weather recognition is 86.9%, the accuracy of the CN-DFR5 algorithm is 97.5%. The latter algorithm is more effective than the former in distinguishing dense fog and non-dense fog from the four types of weather images.
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