基于奇偶交叉卷积的轻量级图像语义分割网络

doi:10.3969/j.issn.0255-8297.2022.03.008

Abstract

Abstract: The multi-step down-sampling convolution in traditional lightweight image semantic segmentation networks easily causes a thorn-like distribution in receptive fields. This will introduce systematic deviations in pixel utilization and finally affect the improvement of segmentation accuracy. Regarding this problem, a down-sampling module of parity cross convolution is designed for traditional lightweight image semantic segmentation networks. In the scheme, an even-convolution module is added before the strided odd-numbered convolution module, in order to alleviate the negative effects caused by spur distribution. Accordingly, the deviation of pixel utilization at different spatial positions in the segmentation network can be eliminated effectively, and the improvement of pixel segmentation accuracy can be finally achieved. Experimental results demonstrate that through the comparison of seven different lightweight image semantic segmentation networks, the proposed model can obviously eliminate the thorn-like distribution and improve the accuracy of the segmentation network. Also, the proposed model performs excellent adaptability to different lightweight networks.

Key words: image semantic segmentation, convolutional neural network (CNN), lightweight network, parity cross convolution

CLC Number:

TP391

LI Fengyong, YE Bin, QIN Chuan. Lightweight Image Semantic Segmentation Network Based on Parity Cross Convolution[J]. Journal of Applied Sciences, 2022, 40(3): 448-456.

References

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Lightweight Image Semantic Segmentation Network Based on Parity Cross Convolution

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