DDCBlock: parallel lightweight modules that focus more on long-distance information

Yunda Liu; Haokun Liu

doi:10.1117/12.3035310

19 July 2024 DDCBlock: parallel lightweight modules that focus more on long-distance information

Yunda Liu, Haokun Liu

Proceedings Volume 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024); 132131J (2024) https://doi.org/10.1117/12.3035310
Event: International Conference on Image Processing and Artificial Intelligence (ICIPAl2024), 2024, Suzhou, China

Abstract

In recent years, artificial intelligence technology has become increasingly closely connected with various fields. However, due to the high requirements of traditional convolutional neural networks for memory and computing resources, it is relatively difficult to deploy them on mobile devices. Therefore, the demand for lightweight neural networks that can be deployed on mobile intelligent terminals is becoming increasingly urgent. This article is inspired by dilated convolution and GhostNetV2, and proposes a new lightweight module-DDC Block. It uses dilated convolution with a larger receptive field structure, combined with depthwise separable convolution, to generate more feature maps through these inexpensive operations, achieving the goal of lightweight. And introduce a decoupled fully connected attention mechanism to ensure high accuracy of the module. We conducted experiments on the CIFAR-10 and CIFAR-100 datasets and compared them with other neural networks. The results showed that this module not only reduced a significant amount of parameter and computational complexity, but also ensured high accuracy.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yunda Liu and Haokun Liu "DDCBlock: parallel lightweight modules that focus more on long-distance information", Proc. SPIE 13213, International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024), 132131J (19 July 2024); https://doi.org/10.1117/12.3035310

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