http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
TypicalVietnameseFoodNet: A Vietnamese Food Image Dataset For Vietnamese Food Classifications
Tri Thien Cao,Khoa Van Duong 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
The process of classifying many types of food from images is an exciting field involving various applications. Especially in tourist, Vietnamese food classification connects us across our cultures and generations. Food classification is not easy, even with people. The reason is the food’s extreme diversity between dishes and in the middle variations of the dish. So some traditional approaches with hand-crafted features had been used for food recognition. However, evaluation in deep learning and convolutional neural networks achieved higher accuracy compared to the traditional methods. We propose a new dataset called TypicalVietnameseFoodNet and a proposed model with the best performance for our dataset, called the TypicalVietnameseFood model. Our proposed approach achieves 94.84% on the test set.
Anh Linh Dang,Tuyen Quang Nguyen,Tri Thien Cao,Vinh Quang Dinh,Vinh Dinh Nguyen 제어로봇시스템학회 2021 제어로봇시스템학회 국제학술대회 논문집 Vol.2021 No.10
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.