Laser & Optoelectronics Progress, Volume. 56, Issue 9, 091003(2019)
Low-Parameter Real-Time Image Segmentation Algorithm Based on Convolutional Neural Network
We propose a real-time image semantic segmentation network model, which is named as Atrous-squeezeseg. Under the condition that the minimum parameter of the model is 2.1×10 7, the operation frame rate is 45.3 frame/s, and the pixel point accuracy and mean intersection over union can reach 59.5% and 62.9%, respectively. At the same time, in the embedded device NVIDIA TX2, the operate frame rate is up to 8.3 frame/s. The experimental results show that, compared with other segmentation algorithms, the speed and parameter quantity of the proposed model are increased.
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Guanghong Tan, Jin Hou, Yanpeng Han, Shuo Luo. Low-Parameter Real-Time Image Segmentation Algorithm Based on Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2019, 56(9): 091003
Category: Image Processing
Received: Oct. 22, 2018
Accepted: Nov. 30, 2018
Published Online: Jul. 5, 2019
The Author Email: Hou Jin (jhou@swjtu.edu.cn)