VGU RESEARCH REPOSITORY
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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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