دورية أكاديمية
Intelligent identification method of mine fire video images based on YOLOv5
العنوان: | Intelligent identification method of mine fire video images based on YOLOv5 |
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المؤلفون: | WANG Weifeng, ZHANG Baobao, WANG Zhiqiang, ZHANG Fangzhi, REN Hao, WANG Jing |
المصدر: | Gong-kuang zidonghua, Vol 47, Iss 9, Pp 53-57 (2021) |
بيانات النشر: | Editorial Department of Industry and Mine Automation, 2021. |
سنة النشر: | 2021 |
المجموعة: | LCC:Mining engineering. Metallurgy |
مصطلحات موضوعية: | mine fire, video image intelligent identification, yolov5, k-means, dark channel defogging algorithm, frame difference method, gaussian mixture model, Mining engineering. Metallurgy, TN1-997 |
الوصف: | In order to solve the problems of video image distortion caused by uneven light distribution and low accuracy of fire identification in coal mines, an intelligent identification method of mine fire video images is proposed. The method uses YOLOv5 as the identification model and uses K-means algorithm to improve the traditional dark channel image defogging algorithm to defog the collected flame images and improve the identification accuracy of mine fire video images. In order to reduce the impact of static background on fire identification, the fusion algorithm of frame difference method and Gaussian mixture model is used to extract the characteristics of the dynamically evolved flame images, and the morphological processing algorithm is used to eliminate the gaps in the images so as to obtain more complete flame target images. The fire video image data set is annotated and input to the YOLOv5 algorithm model for training and testing. The results show that the average accuracy of the intelligent identification method of mine fire video images based on YOLOv5 is 92% with a loss function of 0.6, which is 9.6%, 13.5% and 4.9% higher than that of the traditional algorithms, Alexnet, VGG16 and Inceptionv3 respectively, indicating that this method has fast detection speed and high accuracy, and can improve the accuracy of mine fire identification effectively. |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | Chinese |
تدمد: | 1671-251x 1671-251X |
Relation: | http://www.gkzdh.cn/jn-abD.aspx?ArticleID=15244; https://doaj.org/toc/1671-251X |
DOI: | 10.13272/j.issn.1671-251x.17826 |
URL الوصول: | https://doaj.org/article/6bccf3b919644b57badc0350b3c2d5fc |
رقم الأكسشن: | edsdoj.6bccf3b919644b57badc0350b3c2d5fc |
قاعدة البيانات: | Directory of Open Access Journals |
تدمد: | 1671251x 1671251X |
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DOI: | 10.13272/j.issn.1671-251x.17826 |