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Conference Paper
YOLOX-based fast and lightweight single-class object detector
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- Authors
- Jiwon Lee, Byunggyu Lee, Sung-Uk Jung
- Issue Date
- 2022-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1635-1638
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- 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.
- KSP Keywords
- Class Prediction, Mobile Application(APP), Processing speed, license plate, object detector