| 引用本文: | 刘通,程江华,华宏虎,等.结合YdUaVa颜色模型和改进MobileNetV3的视频烟雾检测方法.[J].国防科技大学学报,2021,43(5):80-85.[点击复制] |
| LIU Tong,CHENG Jianghua,HUA Honghu,et al.Video smoke detection method combining YdUaVa color model and improved MobileNetV3[J].Journal of National University of Defense Technology,2021,43(5):80-85[点击复制] |
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| 结合YdUaVa颜色模型和改进MobileNetV3的视频烟雾检测方法 |
| 刘通,程江华,华宏虎,罗笑冰,程榜 |
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(国防科技大学 电子科学学院, 湖南 长沙 410073)
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| 摘要: |
| 为降低视频烟雾检测中的虚警率和提升检测效率,提出YdUaVa颜色模型,该模型可以表征烟雾的空间域分布特性和时间域变化特性。利用该颜色模型快速筛选出疑似烟雾图像块,降低虚警率和提升运算效率。提出改进的MobileNetV3网络结构,用于提取图像深度特征并对疑似烟雾图像块进行分类识别,检测视频中是否存在烟雾。视频烟雾检测仿真结果表明:该方法准确率和检测帧率高,虚警率低。 |
| 关键词: 烟雾检测 深度学习 颜色模型 轻量级网络 MobileNet |
| DOI：10.11887/j.cn.202105009 |
| 投稿日期：2020-03-17 |
| 基金项目:国家自然科学基金资助项目(61303188);湖南省自然科学基金资助项目(2020JJ4670);基础加强计划技术领域基金资助项目(2019-JCJQ-JJ-209) |
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| Video smoke detection method combining YdUaVa color model and improved MobileNetV3 |
| LIU Tong, CHENG Jianghua, HUA Honghu,LUO Xiaobing, CHENG Bang |
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(College of Electronic Science, National University of Defense Technology, Changsha 410073, China)
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| Abstract: |
| In order to reduce the false alarm rate and improve the detection efficiency for video smoke detection, the YdUaVa color model was proposed, which can characterize the spatial distribution and temporal variation of smoke. By using this color model to quickly screen the suspected smoke image blocks, the false alarm rate was reduced and the computing efficiency was improved. An improved MobileNetV3 network structure was proposed, which is aimed to extract deep features of images and to classify the suspected smoke image blocks so as to detect whether there is smoke in a video. The simulation results of video smoke detection show that this method has high accuracy, high detection frame rate, and low false alarm rate. |
| Keywords: smoke detection deep learning color model lightweight network MobileNet |
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