Image Semantic Segmentation Using Improved ENet Network


Chaoxian Dong, Journal of Information Processing Systems Vol. 17, No. 5, pp. 892-904, Oct. 2021  

10.3745/JIPS.02.0164
Keywords: Bottleneck, Image Semantic Segmentation, Improved ENet, MIOU, MPA, SE Module
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Abstract

An image semantic segmentation model is proposed based on improved ENet network in order to achieve the low accuracy of image semantic segmentation in complex environment. Firstly, this paper performs pruning and convolution optimization operations on the ENet network. That is, the network structure is reasonably adjusted for better results in image segmentation by reducing the convolution operation in the decoder and proposing the bottleneck convolution structure. Squeeze-and-excitation (SE) module is then integrated into the optimized ENet network. Small-scale targets see improvement in segmentation accuracy via automatic learning of the importance of each feature channel. Finally, the experiment was verified on the public dataset. This method outperforms the existing comparison methods in mean pixel accuracy (MPA) and mean intersection over union (MIOU) values. And in a short running time, the accuracy of the segmentation and the efficiency of the operation are guaranteed.


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Cite this article
[APA Style]
Chaoxian Dong (2021). Image Semantic Segmentation Using Improved ENet Network. Journal of Information Processing Systems, 17(5), 892-904. DOI: 10.3745/JIPS.02.0164.

[IEEE Style]
C. Dong, "Image Semantic Segmentation Using Improved ENet Network," Journal of Information Processing Systems, vol. 17, no. 5, pp. 892-904, 2021. DOI: 10.3745/JIPS.02.0164.

[ACM Style]
Chaoxian Dong. 2021. Image Semantic Segmentation Using Improved ENet Network. Journal of Information Processing Systems, 17, 5, (2021), 892-904. DOI: 10.3745/JIPS.02.0164.