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학술대회 Relabeling Method for Improving Vehicle Part Detection
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홍성진, 유초롱, 길연희, 신희숙, 백성민
International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.806-808
22ZH1200, 초실감 입체공간 미디어·콘텐츠 원천기술연구, 이태진
The lightweight deep learning detection method does not properly distinguish partial objects that are named differently depending on their location but has the same shape and appearance like a tire. To solve the problem, this paper introduces a method to relabel the detection result of vehicle parts. We define vehicles parts with super-class and sub-class according to the shape and location, and then detect super-classes. For relabeling super-class to sub-class, we classify vehicle viewing direction, and generate a set of sub-class combinations from super-classes. The spatial distributions among the detected partial objects are analyzed using the likelihood. Then, the labels of the detected parts are determined. We tested our method with self-collected and open dataset, and achieved a mAP value of 87.7%, which is about 11 % better than tiny YOLO v4.
KSP 제안 키워드
Detection Method, Part detection, Spatial distribution, deep learning(DL), open datasets, vehicle part