Laser & Optoelectronics Progress, Volume. 56, Issue 9, 091003(2019)

Low-Parameter Real-Time Image Segmentation Algorithm Based on Convolutional Neural Network

Guanghong Tan, Jin Hou*, Yanpeng Han, and Shuo Luo
Author Affiliations
  • School of Information Science and Technology, Southwest Jiaotong University, Chengdu, Sichuan 611756, China
  • show less

    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.

    Tools

    Get Citation

    Copy Citation Text

    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

    Download Citation

    EndNote(RIS)BibTexPlain Text
    Save article for my favorites
    Paper Information

    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)

    DOI:10.3788/LOP56.091003

    Topics