Deep Semantic Segmentation New Model of Natural and Medical Images

Pei-Yu Chen; Chien-Chieh Huang; Yuan-Chen Liu

doi:10.61927/igmin125

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Peterson Filisbino Prinz, Mariane Hawerroth, Liliane Schier de Lima and Juliana Martins Teixeira de Abreu Pietrobelli

Technology Group Mini Review Article ID: igmin125

Deep Semantic Segmentation New Model of Natural and Medical Images

Machine Learning Image Processing Data Science

Pei-Yu Chen ^* ¹ ,

Chien-Chieh Huang ² and

Yuan-Chen Liu ²

Affiliation

Department of Science Education, College of Science, National Taipei University of Education, Taipei City 10671, Taiwan

Department of Computer Science, College of Science, National Taipei University of Education, Taipei City 10671, Taiwan

DOI10.61927/igmin125 Fulltext HTML Fulltext PDF Cite this article

Abstract

Semantic segmentation is the most significant deep learning technology.
At present, automatic assisted driving (Autopilot) is widely used in real-time driving, but if there is a deviation in object detection in real vehicles, it can easily lead to misjudgment. Turning and even crashing can be quite dangerous. This paper seeks to propose a model for this problem to increase the accuracy of discrimination and improve security. It proposes a Convolutional Neural Network (CNN)+ Holistically-Nested Edge Detection (HED) combined with Spatial Pyramid Pooling (SPP). Traditionally, CNN is used to detect the shape of objects, and the edge may be ignored. Therefore, adding HED increases the robustness of the edge, and finally adds SPP to obtain modules of different sizes, and strengthen the detection of undetected objects. The research results are trained in the CityScapes street view data set. The accuracy of Class mIoU for small objects reaches 77.51%, and Category mIoU for large objects reaches 89.95%.

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References

Shelhamer E, Long J, Darrell T. Fully Convolutional Networks for Semantic Segmentation. IEEE Trans Pattern Anal Mach Intell. 2017 Apr;39(4):640-651. doi: 10.1109/TPAMI.2016.2572683. Epub 2016 May 24. PMID: 27244717.
Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA. 2012; 1:1097–1105.
Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556.
Szegedy C. Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015; 1-9. doi: 10.1109/CVPR.2015.7298594.
He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. arXiv:1512.03385.
Franke U. Making Bertha See, 2013 IEEE International Conference on Computer Vision Workshops. 2013; 214-221. doi: 10.1109/ICCVW.2013.36.
Cakir S, Gauß M, Häppeler K, Ounajjar Y, Heinle F, Marchthaler R. Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability. arXiv:2207.12939. 2022.
Hua M, Nan Y, Lian S. Small Obstacle Avoidance Based on RGB-D Semantic Segmentation. arXiv:1908.11675.
Girisha S, Manohara Pai MM, Verma U, Radhika M Pai. Semantic Segmentation of UAV Videos based on Temporal Smoothness in Conditional Random Fields. 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER). 2020; 241-245. doi: 10.1109 /DISCOVER50404.2020.9278040.
Zhao H, Shi J, Qi X, Wang X, Jia J. Pyramid Scene Parsing Network. arXiv:1612.01105.
Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Trans Pattern Anal Mach Intell. 2018 Apr;40(4):834-848. doi: 10.1109/TPAMI.2017.2699184. Epub 2017 Apr 27. PMID: 28463186.
Chen LC, Papandreou G, Schroff F, Adam H. Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv:1706.05587.
He K, Zhang X, Ren S, Sun J. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. arXiv:1406.4729.
Chen LC, Papandreou G, Schroff F, Adam H. Rethinking Atrous Convolution for Semantic Image Segmentation. 2017.
Heidler K, Mou L, Baumhoer C, Dietz A, Zhu XX. HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline. arXiv:2103.01849.
Takikawa T, Acuna D, Jampani V, Fidler S. Gated-SCNN: Gated Shape CNNs for Semantic Segmentation. arXiv:1907.05740.

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[1] Shelhamer E, Long J, Darrell T. Fully Convolutional Networks for Semantic Segmentation. IEEE Trans Pattern Anal Mach Intell. 2017 Apr;39(4):640-651. doi: 10.1109/TPAMI.2016.2572683. Epub 2016 May 24. PMID: 27244717.

[2] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In Proceedings of the 25th International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA. 2012; 1:1097–1105.

[3] Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556.

[4] Szegedy C. Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2015; 1-9. doi: 10.1109/CVPR.2015.7298594.

[5] He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. arXiv:1512.03385.

[6] Franke U. Making Bertha See, 2013 IEEE International Conference on Computer Vision Workshops. 2013; 214-221. doi: 10.1109/ICCVW.2013.36.

[7] Cakir S, Gauß M, Häppeler K, Ounajjar Y, Heinle F, Marchthaler R. Semantic Segmentation for Autonomous Driving: Model Evaluation, Dataset Generation, Perspective Comparison, and Real-Time Capability. arXiv:2207.12939. 2022.

[8] Hua M, Nan Y, Lian S. Small Obstacle Avoidance Based on RGB-D Semantic Segmentation. arXiv:1908.11675.

[9] Girisha S, Manohara Pai MM, Verma U, Radhika M Pai. Semantic Segmentation of UAV Videos based on Temporal Smoothness in Conditional Random Fields. 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER). 2020; 241-245. doi: 10.1109 /DISCOVER50404.2020.9278040.

[10] Zhao H, Shi J, Qi X, Wang X, Jia J. Pyramid Scene Parsing Network. arXiv:1612.01105.

[11] Chen LC, Papandreou G, Kokkinos I, Murphy K, Yuille AL. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Trans Pattern Anal Mach Intell. 2018 Apr;40(4):834-848. doi: 10.1109/TPAMI.2017.2699184. Epub 2017 Apr 27. PMID: 28463186.

[12] Chen LC, Papandreou G, Schroff F, Adam H. Rethinking Atrous Convolution for Semantic Image Segmentation. arXiv:1706.05587.

[13] He K, Zhang X, Ren S, Sun J. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. arXiv:1406.4729.

[14] Chen LC, Papandreou G, Schroff F, Adam H. Rethinking Atrous Convolution for Semantic Image Segmentation. 2017.

[15] Heidler K, Mou L, Baumhoer C, Dietz A, Zhu XX. HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline. arXiv:2103.01849.

[16] Takikawa T, Acuna D, Jampani V, Fidler S. Gated-SCNN: Gated Shape CNNs for Semantic Segmentation. arXiv:1907.05740.