• 대한임베디드공학회논문지
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• 제19권5호
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• pp.227-234
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• 2024

This paper proposes a novel method for effectively classifying 12 vehicle types required for road traffic surveys by utilizing deep learning techniques. In particular, it focuses on the trailer vehicle types, classified as types 8 to 12, which have been challenging in previous research due to data scarcity. A zero-shot learning approach, Grounding DINO, is employed to extract key features that can distinguish these trailer types, addressing the data imbalance issue. This method enables accurate classification of the underrepresented vehicle types, leading to efficient classification across all 12 types. To the best of the authors' knowledge, this is the first attempt to classify 12 vehicle types required for road traffic surveys using publicly available video data.