Infrared image recognition plays an important role in the inspection of power equipment.Existing technologies dedicated to this purpose often require manually selected features,which are not transferable and interpret...Infrared image recognition plays an important role in the inspection of power equipment.Existing technologies dedicated to this purpose often require manually selected features,which are not transferable and interpretable,and have limited training data.To address these limitations,this paper proposes an automatic infrared image recognition framework,which includes an object recognition module based on a deep self-attention network and a temperature distribution identification module based on a multi-factor similarity calculation.First,the features of an input image are extracted and embedded using a multi-head attention encoding-decoding mechanism.Thereafter,the embedded features are used to predict the equipment component category and location.In the located area,preliminary segmentation is performed.Finally,similar areas are gradually merged,and the temperature distribution of the equipment is obtained to identify a fault.Our experiments indicate that the proposed method demonstrates significantly improved accuracy compared with other related methods and,hence,provides a good reference for the automation of power equipment inspection.展开更多
Improving the quality of equipment training for the Heavy Equipment Operators(HEO)is a critical task in improving safety and eliminating equipment-related injuries in mining.One of major responsibilities for the HEOs ...Improving the quality of equipment training for the Heavy Equipment Operators(HEO)is a critical task in improving safety and eliminating equipment-related injuries in mining.One of major responsibilities for the HEOs is proper machine inspection.Traditional miner safety training includes the use of hardcopy documents and video instructions.However,modern mobile and computer technology offers tremendous potential to improve the training process.In this study,we apply a 360-degree camera,opensource platform WordPress^(TM),and the software Unity3D in order to create materials and tools for the HEOs safety training to help trainees better understand the pre-shift safety machine inspection.The computer-based safety task training developed in this research is tested and implemented at a surface mine in the southern United States.展开更多
基金This work was supported by National Key R&D Program of China(2019YFE0102900).
文摘Infrared image recognition plays an important role in the inspection of power equipment.Existing technologies dedicated to this purpose often require manually selected features,which are not transferable and interpretable,and have limited training data.To address these limitations,this paper proposes an automatic infrared image recognition framework,which includes an object recognition module based on a deep self-attention network and a temperature distribution identification module based on a multi-factor similarity calculation.First,the features of an input image are extracted and embedded using a multi-head attention encoding-decoding mechanism.Thereafter,the embedded features are used to predict the equipment component category and location.In the located area,preliminary segmentation is performed.Finally,similar areas are gradually merged,and the temperature distribution of the equipment is obtained to identify a fault.Our experiments indicate that the proposed method demonstrates significantly improved accuracy compared with other related methods and,hence,provides a good reference for the automation of power equipment inspection.
文摘Improving the quality of equipment training for the Heavy Equipment Operators(HEO)is a critical task in improving safety and eliminating equipment-related injuries in mining.One of major responsibilities for the HEOs is proper machine inspection.Traditional miner safety training includes the use of hardcopy documents and video instructions.However,modern mobile and computer technology offers tremendous potential to improve the training process.In this study,we apply a 360-degree camera,opensource platform WordPress^(TM),and the software Unity3D in order to create materials and tools for the HEOs safety training to help trainees better understand the pre-shift safety machine inspection.The computer-based safety task training developed in this research is tested and implemented at a surface mine in the southern United States.
