Open AccessFlsnet: Fast and Light Segmentation Network
Author(s) -
Qian Sun,
Wei Chen,
Jiangang Chao,
Hongbo Zhang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1518/1/012047
Subject(s) - segmentation , computer science , encoder , artificial intelligence , deep learning , separable space , image segmentation , channel (broadcasting) , pattern recognition (psychology) , computer vision , mathematics , computer network , mathematical analysis , operating system
The use of deep learning for image segmentation has proven to be an efficient and accurate method, but with the complexity of the network structure, it takes up a lot of computing resources. The consumption of computing resources may be unacceptable during tasks. Aiming at this problem, a fast and light segmentation network (FLSNet) is proposed, which uses the Encoder-Decoder method to extract features. All convolutional layers use depthwise separable convolutions and the channel attention module is linked between Encoder and Decoder. Experiments are performed on the autonomous driving dataset CamVid. The results show that with a slight increase in segmentation accuracy, the model size becomes 8.65% of SegNet, the required computing resources are reduced by a dozen times, and the segmentation speed is increased by about 12%, which show that our network is efficient.