The airport apron scene contains rich contextual information about the spatial position relationship.Traditional object detectors only considered visual appearance and ignored the contextual information.In addition,th...The airport apron scene contains rich contextual information about the spatial position relationship.Traditional object detectors only considered visual appearance and ignored the contextual information.In addition,the detection accuracy of some categories in the apron dataset was low.Therefore,an improved object detection method using spatial-aware features in apron scenes called SA-FRCNN is presented.The method uses graph convolutional networks to capture the relative spatial relationship between objects in the apron scene,incorporating this spatial context into feature learning.Moreover,an attention mechanism is introduced into the feature extraction process,with the goal to focus on the spatial position and key features,and distance-IoU loss is used to achieve a more accurate regression.The experimental results show that the mean average precision of the apron object detection based on SAFRCNN can reach 95.75%,and the detection effect of some hard-to-detect categories has been significantly improved.The proposed method effectively improves the detection accuracy on the apron dataset,which has a leading advantage over other methods.展开更多
This study aims to identify the current situation and problems of environmental information statement for major four home appliances （refrigerators, washing machines, air conditioners and television receivers） sold ...This study aims to identify the current situation and problems of environmental information statement for major four home appliances （refrigerators, washing machines, air conditioners and television receivers） sold at online stores in Japan, and then to suggest how to improve the situation, through a questionnaire survey conducted among businesses that operate online stores and online malls with multiple online stores. The findings of this study are summarized into the following two points： （l） It is found out that environmental information statement for the home appliances at online stores has four problems： （i） less information on ＂three Rs （reduce, reuse and recycle）＂ and ＂chemical substances＂ than the one on ＂energy conservation＂; （ii） cost for providing environmental information statement; （iii） issues associated with a label and mark placement; （iv） issues associated with energy conservation statement. （2） Improvements are suggested for each of the four problems listed above, and shown are （i） the effectiveness of, and need to promote, a label and mark placement; （ii） cost burden on buyers; （iii） need of active efforts made by businesses and of dissemination of legal regulations to businesses.展开更多
Dense captioning aims to simultaneously localize and describe regions-of-interest(RoIs)in images in natural language.Specifically,we identify three key problems:1)dense and highly overlapping RoIs,making accurate loca...Dense captioning aims to simultaneously localize and describe regions-of-interest(RoIs)in images in natural language.Specifically,we identify three key problems:1)dense and highly overlapping RoIs,making accurate localization of each target region challenging;2)some visually ambiguous target regions which are hard to recognize each of them just by appearance;3)an extremely deep image representation which is of central importance for visual recognition.To tackle these three challenges,we propose a novel end-to-end dense captioning framework consisting of a joint localization module,a contextual reasoning module and a deep convolutional neural network(CNN).We also evaluate five deep CNN structures to explore the benefits of each.Extensive experiments on visual genome(VG)dataset demonstrate the effectiveness of our approach,which compares favorably with the state-of-the-art methods.展开更多
基金supported by the Fundamental Research Funds for Central Universities of the Civil Aviation University of China(No.3122021088).
文摘The airport apron scene contains rich contextual information about the spatial position relationship.Traditional object detectors only considered visual appearance and ignored the contextual information.In addition,the detection accuracy of some categories in the apron dataset was low.Therefore,an improved object detection method using spatial-aware features in apron scenes called SA-FRCNN is presented.The method uses graph convolutional networks to capture the relative spatial relationship between objects in the apron scene,incorporating this spatial context into feature learning.Moreover,an attention mechanism is introduced into the feature extraction process,with the goal to focus on the spatial position and key features,and distance-IoU loss is used to achieve a more accurate regression.The experimental results show that the mean average precision of the apron object detection based on SAFRCNN can reach 95.75%,and the detection effect of some hard-to-detect categories has been significantly improved.The proposed method effectively improves the detection accuracy on the apron dataset,which has a leading advantage over other methods.
文摘This study aims to identify the current situation and problems of environmental information statement for major four home appliances （refrigerators, washing machines, air conditioners and television receivers） sold at online stores in Japan, and then to suggest how to improve the situation, through a questionnaire survey conducted among businesses that operate online stores and online malls with multiple online stores. The findings of this study are summarized into the following two points： （l） It is found out that environmental information statement for the home appliances at online stores has four problems： （i） less information on ＂three Rs （reduce, reuse and recycle）＂ and ＂chemical substances＂ than the one on ＂energy conservation＂; （ii） cost for providing environmental information statement; （iii） issues associated with a label and mark placement; （iv） issues associated with energy conservation statement. （2） Improvements are suggested for each of the four problems listed above, and shown are （i） the effectiveness of, and need to promote, a label and mark placement; （ii） cost burden on buyers; （iii） need of active efforts made by businesses and of dissemination of legal regulations to businesses.
基金Project(2020A1515010718)supported by the Basic and Applied Basic Research Foundation of Guangdong Province,China。
文摘Dense captioning aims to simultaneously localize and describe regions-of-interest(RoIs)in images in natural language.Specifically,we identify three key problems:1)dense and highly overlapping RoIs,making accurate localization of each target region challenging;2)some visually ambiguous target regions which are hard to recognize each of them just by appearance;3)an extremely deep image representation which is of central importance for visual recognition.To tackle these three challenges,we propose a novel end-to-end dense captioning framework consisting of a joint localization module,a contextual reasoning module and a deep convolutional neural network(CNN).We also evaluate five deep CNN structures to explore the benefits of each.Extensive experiments on visual genome(VG)dataset demonstrate the effectiveness of our approach,which compares favorably with the state-of-the-art methods.
