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Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1594
DC FieldValueLanguage
dc.contributor.authorDang Anh Linhen_US
dc.contributor.authorNguyen Tuyen Quangen_US
dc.contributor.authorCao Tri Thienen_US
dc.contributor.authorDinh Vinh Quangen_US
dc.contributor.authorNguyen Vinh Dinhen_US
dc.date.accessioned2023-07-25T02:37:20Z-
dc.date.available2023-07-25T02:37:20Z-
dc.date.issued2021-
dc.identifier.urihttps://epub.vgu.edu.vn/handle/dlibvgu/1594-
dc.description.abstractTraffic detection is a topic of great interest in recent years due to a high demand for better traffic detection systems. Existing traffic detection algorithms work well under ideal driving conditions, however their performance decreases under difficult conditions such as insufficient lighting and illumination. Recently, local patterns have been successfully applied in order to handle complex texture conditions, such as stereo matching, and texture classification. We propose a method that applies Local Tetra Pattern for data preprocessing, so as to improve the performance of deep learning models under said conditions. Our approach achieved better performance than the original raw-models while the changes in inference time are maintained within a negligible interval. By fusing local patterns and raw images, the model gains an acquisition of discriminative information in regions that are highly similar. In challenging conditions, these kinds of information are essential for the model to recover its consciousness of concerned objects which cause many re-cognitional obstructions. Experimental results show a percentage as high as 35.847%, an increase of 12.575% in comparison with the original result on the SKKU data set.en_US
dc.language.isoenen_US
dc.subjectLocal patternen_US
dc.subjectObject detectionen_US
dc.subjectHostile conditionsen_US
dc.titleLocal tetra pattern and its benefits to improve the performance of car and pedestrian detection under hostile conditionsen_US
dc.typeProceedingsen_US
dc.relation.conferenceThe 21st International Conference on Control, Automation and Systemsen_US
dc.identifier.doihttps://doi.org/10.23919/ICCAS52745.2021.9649941-
dc.relation.duration12-15/10/2021en_US
dc.relation.conferencevenueJeju, Koreaen_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.languageiso639-1other-
Appears in Collections:Conference papers
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