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VGU RESEARCH REPOSITORY


Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1594
Title: Local tetra pattern and its benefits to improve the performance of car and pedestrian detection under hostile conditions
Authors: Dang Anh Linh 
Nguyen Tuyen Quang 
Cao Tri Thien 
Dinh Vinh Quang 
Nguyen Vinh Dinh 
Keywords: Local pattern;Object detection;Hostile conditions
Issue Date: 2021
Conference name: The 21st International Conference on Control, Automation and Systems 
Abstract: 
Traffic 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.
Duration: 12-15/10/2021
Conference venue: Jeju, Korea
URI(1): https://epub.vgu.edu.vn/handle/dlibvgu/1594
DOI: https://doi.org/10.23919/ICCAS52745.2021.9649941
Appears in Collections:Conference papers

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