The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
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Lightweight Residual Layer Based Convolutional Neural Networks for Traffic Sign Recognition

교통 신호 인식을 위한 경량 잔류층 기반 컨볼루션 신경망

  • ;
  • 류재흥 (전남대학교 컴퓨터공학과)
  • Received : 2022.01.03
  • Accepted : 2022.02.17
  • Published : 2022.02.28
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Abstract

Traffic sign recognition plays an important role in solving traffic-related problems. Traffic sign recognition and classification systems are key components for traffic safety, traffic monitoring, autonomous driving services, and autonomous vehicles. A lightweight model, applicable to portable devices, is an essential aspect of the design agenda. We suggest a lightweight convolutional neural network model with residual blocks for traffic sign recognition systems. The proposed model shows very competitive results on publicly available benchmark data.

교통 표지 인식은 교통 관련 문제를 해결하는 데 중요한 역할을 한다. 교통 표지 인식 및 분류 시스템은 교통안전, 교통 모니터링, 자율주행 서비스 및 자율주행 차의 핵심 구성 요소이다. 휴대용 장치에 적용할 수 있는 경량 모델은 설계 의제의 필수 측면이다. 우리는 교통 표지 인식 시스템을 위한 잔여 블록이 있는 경량 합성곱 신경망 모델을 제안한다. 제안된 모델은 공개적으로 사용 가능한 벤치마크 데이터에서 매우 경쟁력 있는 결과를 보여준다.

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References

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