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RISS 인기검색어
- 검색키워드
- 저자 : Yingpeng Dai (검색결과 2 건)
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PDBNet: Parallel Dual Branch Network for Real-time Semantic Segmentation
Yingpeng Dai,Junzheng Wang,Jiehao Li,Jing Li 제어·로봇·시스템학회 2022 International Journal of Control, Automation, and Vol.20 No.8
To make a trade-off between accuracy and inference speed in real-time applications on the unmanned mobile platform, a novel neural network, named Parallel Dual Branch Network (PDBNet), is proposed. Firstly, a multi-scale module, namely Parallel Dual Branch (PDB), is designed to extract complete information. PDB module consists of two parallel branches to remove detailed low-level information and high-level semantic information while maintaining few parameters. Then, based on the PDB module, PDBNet, a small-scale and shallow structure, is designed for semantic segmentation. A multi-scale module tends to extract abundant information and segment the object out from the image well. The small-scale and shallow structure tends to accelerate the inference speed. So PDBNet architecture is designed to be effective both in terms of accuracy and inference speed. PDBNet adopts three downsamplings to obtain feature maps with high spatial resolution and uses PDB modules with different dilation rates to extract multi-scale features and enlarge the receptive field in the last several layers. Finally, experiments on Camvid dataset and Cityscapes dataset, we respectively get 67.7% and 69.5% Mean Intersection over Union (MIoU) with only 1.82 million parameters and quicker speed on a single GTX 1070Ti card.
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Junrui Xue,Yutan Wang,Aili Qu,Yingpeng Dai 제어·로봇·시스템학회 2023 International Journal of Control, Automation, and Vol.21 No.6
Semantic segmentation is an effective means for autonomous robots to understand the surrounding scenes. For autonomous robot, it requires the balance of accuracy and speed. Moreover, it is necessary to correctly extract environmental information in complex environments such as occlusion, poor illumination, and shadows condition. To solve above problems, a novel image-based Multi-scale Feature Extraction Network (MFENet) is designed for real-time semantic segmentation task. This network preserves different level features in the encoder and fuses those features to accurately segment each object. In addition, to enhance the representation ability, fusion module is introduced for information exchange between feature maps with different spatial resolution. Moreover, standard convolution is replaced by Multiscale Feature Extraction (MFE) module in intermediate layers, which could strengthen the feature extraction ability. On the Cityscapes dataset, MFENet achieves 72.4% Mean Intersection over Union (MIoU) with 8.0 million parameters at the speed of 30.5 FPS on a single GTX 1070Ti card. Finally, MFENet is deployed on an autonomous robot and tested in the real world. It produces good semantic segmentation results at the speed of 65.5 FPS. The results reveals the proposed MFENet could work well in real-world applications.
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