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- W4310009591 abstract "In general, an object detector performs a series of processes finding regions estimated as objects in the image and specifying them as a predefined class. However, in the case of detecting only a single class such as a pedestrian detector or a license plate detector, it may not classify the estimated regions. Based on this idea, we developed a fast and lightweight single-class object detector with high processing speed while minimizing the loss of accuracy by excluding the class prediction layer from the YOLOX network and training the regression layer to respond only to the target class. As can be seen from the experimental results, the proposed method shows similar precision while having a faster inference speed compared to the original YOLOX model. It can be applied to mobile applications that lack resources but require a fast processing speed." @default.
- W4310009591 created "2022-11-30" @default.
- W4310009591 creator A5034622929 @default.
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- W4310009591 date "2022-10-19" @default.
- W4310009591 modified "2023-09-29" @default.
- W4310009591 title "YOLOX-based fast and lightweight single-class object detector" @default.
- W4310009591 cites W2963037989 @default.
- W4310009591 doi "https://doi.org/10.1109/ictc55196.2022.9952952" @default.
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