[1]赵崇志,卢 俊,田 源,等.基于 Yolov5s 的作业现场阀门规范操作识别方法[J].计算机技术与发展,2022,32(05):216-220.[doi:10. 3969 / j. issn. 1673-629X. 2022. 05. 036]
　ZHAO Chong-zhi,LU Jun,TIAN Yuan,et al.Yolov5s-based Method for Identifying Valve Specification Operations at Job Sites[J].,2022,32(05):216-220.[doi:10. 3969 / j. issn. 1673-629X. 2022. 05. 036]
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基于 Yolov5s 的作业现场阀门规范操作识别方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
32
期数:
2022年05期
页码:
216-220
栏目:
应用前沿与综合
出版日期:
2022-05-10

文章信息/Info

Title:
Yolov5s-based Method for Identifying Valve Specification Operations at Job Sites
文章编号:
1673-629X(2022)05-0216-05
作者:
赵崇志1 ; 卢 俊2 ; 田 源1 ; 邢金台1 ; 董金平1 ; 张 蕾1 ; 田 枫2
1. 中国石油天然气股份有限公司冀东油田分公司,河北 唐山 063004;
2. 东北石油大学 计算机与信息技术学院,黑龙江 大庆 163318
Author(s):
ZHAO Chong-zhi1 ; LU Jun2 ; TIAN Yuan1 ; XING Jin-tai1 ; DONG Jin-ping1 ; ZHANG Lei1 ; TIAN Feng2
1. Jidong Oilfield Company,PetroChina,Tangshan 063004,China;
2. School of Computer & Information Technology of Northeast Petroleum University,Daqing 163318,China
关键词:
深度学习; 作业现场; YOLOv5s; 目标检测; 阀门操作识别
Keywords:
deep learning; operation sites; YOLOv5s; object detection; valve operation identification
分类号:
TP399
DOI:
10. 3969 / j. issn. 1673-629X. 2022. 05. 036
摘要:
将基于深度学习算法的图像识别技术应用到油田作业现场监控领域, 解决阀门操作的规范性问题。 采用YOLOv5s 网络作为作业现场阀门规范操作识别的模型,通过图像增强方法解决阀门周围的栏杆遮挡问题,制作阀门操作的数据集,保证了数据集的丰富性。 然后在自制阀门操作分类的数据集上对网络进行训练,利用训练好的 YOLOv5s 网络提取作业人员不同的阀门操作图像的特征和位置信息,实现作业现场阀门规范操作的识别。 经过测试,验证该模型最终检测准确率达到了 93% ,检测速度能达到实时的效果。 基于 YOLOv5s 网络的作业现场阀门规范操作识别的模型在不同光照和视角等条件下,检测准确率高,鲁棒性好、模型计算速度快。 满足了油田作业现场实际需求,解决了油田作业现场员工在阀门操作上的安全问题。
Abstract:
To apply the image recognition technology based on deep learning algorithm to the field of oilfield operation site monitoring to solve the problem? of valve operation specification. In this paper,the YOLOv5s network is used as a model for the recognition of valve normative operations at operation sites, and the problem of obscuring the railings around the valves is solved by image enhancement methods to produce a dataset? ? ? ? of valve operations, which ensures the richness of the dataset. The network is then trained on the dataset of homemade valve operation classification, and the trained YOLOv5s network is used to extract the features and location information of different valve operation images of operators to achieve the recognition of valve regulation operation at the job site. After testing,it was verified that the final detection accuracy of? ?the model reached 93% and the detection speed could achieve real - time results. The YOLOv5s network-based model for the recognition of regulated valve operations at the job site has high detection accuracy, strong robustness and fast model computation under different lighting and viewing angles,which meets the actual needs of oilfield operation sites and solves the safety problems of oilfield operation site employees in valve operation.

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更新日期/Last Update: 2022-05-10