Power production is usually faced with complex working environment conversion such as high and low voltage,strong and weak current,and different operation scenarios have strict standards for the use of protection tools.Therefore,it is of great significance to study the fine identification of protection tools in the production process to ensure the safety of personnel and even the safe operation of power grid.Existing methods can realize the detection of basic clothing such as hats and work clothes.However,there are physical protection tools with highly similar shapes in actual production,such as insulating gloves and cotton gloves,insulating poles and testing poles.Therefore,this paper proposes an intelligent detection method of similar protection tools based on deep representative metric learning.The target category feature learning is transformed into embedded spatial feature learning to express the feature distances of different targets,to obtain the deep representative feature vectors representing different targets.By calculating the distance between the unknown target and the representative feature vectors to realize protection tools identification.Finally,the experimental verification is carried out with the images collected in the field.The results show that proposed method realizes the feature difference expression and accurate identification of similar protection tools,and has superior identification performance compared with common target detection models,thus improving the refined level of power production safety risk identification.
关键词
生产安全防护/安全影像解译/电力深度视觉/高度相似目标/深度度量学习/嵌入特征空间
Key words
production safety protection/safety image interpretation/power depth vision/highly similar targets/deep metric learning/embedded feature space
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