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The UQTR dataset consists of 7838 real and synthetic images of the Université du Québec à Trois-Rivières (UQTR) campus road under normal and snow conditions. The image resolution is 1280×720. It includes lane labels in .txt files, where each row stores the set of points of a lane. The points are stored as x1 y1 x2 y2, as in the tutorial by Ruijin Liu, Zejian Yuan, Tie Liu, Zhiliang Xiong: Train and Test Your Custom Data.
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Infrared dim-small target detection has gained increasing importance in both military and civilian applications due to its ability to detect thermal radiation, operate effectively at night, passively sense radiation, and offer strong concealment with high resistance to interference. These capabilities make it ideal for systems such as aircraft and bird surveillance, missile guidance, and maritime rescue operations. In these applications, the need for mid- to long-range observations often results in small targets that appear dim and are difficult to detect.
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Sign language correctness discrimination (SLCD) dataset is collected for sign language teaching. Different from general sign language recognition datasets, SLCD dataset has two kind labels of sign language category and standardization category at the same time. The standardization category is to describe action correctness of the same sign language made by students. The SLCD dataset videos in this paper are obtained by camera. 76 students are recruited to collect sign language actions.
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