Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Concurrent Detection for Vehicles and Lanes Using Light-Weight Model of Multi-Task CNN

멀티 테스크 CNN의 경량화 모델을 이용한 차량 및 차선의 동시 검출

  • Shin, Hyeon-Sik (College of Electrical & Computer Engineering, ChungBuk University) ;
  • Kim, Hyung-Won (College of Electrical & Computer Engineering, ChungBuk University) ;
  • Hong, Sang-Wook (College of Electrical & Computer Engineering, ChungBuk University)
  • Received : 2021.12.23
  • Accepted : 2021.12.29
  • Published : 2022.03.31
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Abstract

As deep learning-based autonomous driving technology develops, artificial intelligence models for various purposes have been studied. Based on these studies, several models were used simultaneously to develop autonomous driving systems. It can occur by increasing hardware resource consumption. We propose a multi-tasks model using a shared backbone to solve this problem. This can solve the increase in the number of backbones for using AI models. As a result, in the proposed lightweight model, the model parameters could be reduced by more than 50% compared to the existing model, and the speed could be improved. In addition, each lane can be classified through lane detection using the instance segmentation method. However, further research is needed on the decrease in accuracy compared to the existing model.

딥러닝 기반 자율 주행 기술이 발전함에 따라 다양한 목적의 인공지능 모델이 연구되었다. 연구된 여러 모델들을 동시에 구동하여 자율주행 시스템을 개발한다. 그러나 동시에 인공지능 모델을 사용하면서 많은 하드웨어 자원 소비가 증가한다. 이를 해결하기 위해 본 논문은 백본 모델을 공유하며 다중 태스크를 고속으로 수행할 수 있는 Multi-Task CNN 모델을 제안한다. 이를 통해 AI모델을 사용하기 위한 백본 수의 증가를 해결할 수 있었습니다. 제안하는 CNN 모델은 기존 모델 대비 50% 이상 웨이트 파라미터 수를 감소시키며, 3배 이상의 FPS 속도를 향상시켰다. 또한, 차선인식은 Instance segmentation 기반으로 차선검출 및 차선별 Labeling을 모두 출력한다. 그러나 기존 모델에 비해 정확도가 감소하는 부분에 대해서는 추가적인 연구가 필요하다.

Keywords

Acknowledgement

This work was partly supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2020-0-01304, Development of Self-learnable Mobile Recursive Neural Network Processor Technology) and also supported by the MSIT(Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program(IITP-2022-2020-0-01462) and supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation)". and also supported by the AURI(Korea Association of University, Research institute and Industry) grant funded by the Korea Government(MSS : Ministry of SMEs and Startups). (No. S2929950, HRD program for 2020)

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