DC-UNet: research on image segmentation based on deep learning

Lu Huang; Tingting Song; Kang Yu; Fengen Yuan; Huaqiang Wang; Junnan Zhi; Guangyang Hu; Hao Yang

doi:10.1117/12.2660623

30 November 2022 DC-UNet: research on image segmentation based on deep learning

Lu Huang, Tingting Song, Kang Yu, Fengen Yuan, Huaqiang Wang, Junnan Zhi, Guangyang Hu, Hao Yang

Proceedings Volume 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022); 124562G (2022) https://doi.org/10.1117/12.2660623
Event: International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 2022, Qingdao, China

Abstract

In recent years, deep learning has developed very fast. Since the introduction of deep learning, good research results have been achieved in the field of computer vision. In the era of artificial intelligence, computer vision technology has been widely used in fields such as unmanned driving and security monitoring. The birth of these technologies is based on image segmentation. Image segmentation algorithms based on deep learning are constantly being proposed, which have made a qualitative leap in performance and effect compared with traditional image segmentation algorithms, but there is still a lot of room for improvement. This paper improves on the U-net, a variant of the classic fully convolutional neural network. Combining U-net with atrous convolution, a new network model for image segmentation is proposed. In order to verify the actual segmentation effect of the new network model, this paper is tested on the public image segmentation data set, and compared with other classic image segmentation algorithms. The experimental results prove that the network model has a good segmentation effect.

Citation Download Citation

Lu Huang, Tingting Song, Kang Yu, Fengen Yuan, Huaqiang Wang, Junnan Zhi, Guangyang Hu, and Hao Yang "DC-UNet: research on image segmentation based on deep learning", Proc. SPIE 12456, International Conference on Artificial Intelligence and Intelligent Information Processing (AIIIP 2022), 124562G (30 November 2022); https://doi.org/10.1117/12.2660623

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KEYWORDS

Image segmentation

Convolution

Image processing algorithms and systems

Detection and tracking algorithms

Evolutionary algorithms

Computer vision technology

Computer programming

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