상세정보
학술대회
YOLOX-based fast and lightweight single-class object detector
- 발행일
- 202210
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1635-1638
- 협약과제
- 22HH9200, 실·가상 환경 해석 기반 적응형 인터랙션 기술 개발, 정성욱
- 초록
- 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 제안 키워드
- Class Prediction, Mobile Application(APP), Processing speed, license plate, object detector