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Real-time Lightweight CNN for Detecting Road Object of Various Size
- Lim, Byeonghak;
- Yang, Bin;
- Kim, Hakil
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6초록
This paper proposed a novel lightweight convolutional neural network suitable for road object detection which not only for small objects, but for large objects. The proposed network outperformed detection performance of existing convolutional neural networks on KITTI datasets and satisfied real-time processing speed of 10ms on PC and 65ms on NVIDIA TX2. The model is suitable for running in an embedded environment with only 3-million weight parameters.
키워드
road object detection; lightweight CNN; various object sizes; real-time embedded CNN
- 제목
- Real-time Lightweight CNN for Detecting Road Object of Various Size
- 저자
- Lim, Byeonghak; Yang, Bin; Kim, Hakil
- 발행일
- 2018
- 유형
- Proceedings Paper
- 저널명
- IEEE 1ST CONFERENCE ON MULTIMEDIA INFORMATION PROCESSING AND RETRIEVAL (MIPR 2018)
- 페이지
- 202 ~ 203