{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T16:07:16Z","timestamp":1780330036593,"version":"3.54.1"},"reference-count":34,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,5,21]],"date-time":"2020-05-21T00:00:00Z","timestamp":1590019200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology (MOST), Taiwan","award":["MOST 107-2221-E-155-048-MY3"],"award-info":[{"award-number":["MOST 107-2221-E-155-048-MY3"]}]},{"name":"Ministry of Science and Technology (MOST), Taiwan","award":["MOST 108-2634-F-008-001"],"award-info":[{"award-number":["MOST 108-2634-F-008-001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Semantic segmentation of street view images is an important step in scene understanding for autonomous vehicle systems. Recent works have made significant progress in pixel-level labeling using Fully Convolutional Network (FCN) framework and local multi-scale context information. Rich global context information is also essential in the segmentation process. However, a systematic way to utilize both global and local contextual information in a single network has not been fully investigated. In this paper, we propose a global-and-local network architecture (GLNet) which incorporates global spatial information and dense local multi-scale context information to model the relationship between objects in a scene, thus reducing segmentation errors. A channel attention module is designed to further refine the segmentation results using low-level features from the feature map. Experimental results demonstrate that our proposed GLNet achieves 80.8% test accuracy on the Cityscapes test dataset, comparing favorably with existing state-of-the-art methods.<\/jats:p>","DOI":"10.3390\/s20102907","type":"journal-article","created":{"date-parts":[[2020,5,21]],"date-time":"2020-05-21T11:31:18Z","timestamp":1590060678000},"page":"2907","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Global-and-Local Context Network for Semantic Segmentation of Street View Images"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0401-8473","authenticated-orcid":false,"given":"Chih-Yang","family":"Lin","sequence":"first","affiliation":[{"name":"Department of Electrical Engineering, Yuan Ze University, Taoyuan 32003, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi-Cheng","family":"Chiu","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Information Engineering, National Central University, Taoyuan City 32001, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4394-2770","authenticated-orcid":false,"given":"Hui-Fuang","family":"Ng","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University Tunku Abdul Rahman, Kampar 31900, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy K.","family":"Shih","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Information Engineering, National Central University, Taoyuan City 32001, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kuan-Hung","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Computer Science &amp; Information Engineering, National Central University, Taoyuan City 32001, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sharma, S., Ball, J., Tang, B., Carruth, D., Doude, M., and Islam, M.A. (2019). Semantic Segmentation with Transfer Learning for Off-Road Autonomous Driving. Sensors, 19.","DOI":"10.3390\/s19112577"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"S\u00e1ez, \u00c1., Bergasa, L.M., L\u00f3pez-Guill\u00e9n, E., Romera, E., Tradacete, M., G\u00f3mez-Hu\u00e9lamo, C., and Del Egido, J. (2019). Real-Time Semantic Segmentation for Fisheye Urban Driving Images Based on ERFNet \u2020. Sensors, 19.","DOI":"10.3390\/s19030503"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 11\u201318). Learning deconvolution network for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_5","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., and Yuille, A.L. (2014). Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","unstructured":"Yu, F., and Koltun, V. (2015). Multi-scale context aggregation by dilated convolutions. arXiv."},{"key":"ref_8","unstructured":"Chen, L.-C., Yang, Y., Wang, J., Xu, W., and Yuille, A.L. (July, January 26). Attention to scale: Scale-aware semantic image segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Eigen, D., and Fergus, R. (2015, January 11\u201318). Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.304"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhao, H., Qi, X., Shen, X., Shi, J., and Jia, J. (2018, January 8\u201314). ICNeT for real-time semantic segmentation on high-resolution images. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01219-9_25"},{"key":"ref_11","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Yang, M., Yu, K., Zhang, C., Li, Z., and Yang, K. (2018, January 18\u201323). DenseASPP for semantic segmentation in street scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00388"},{"key":"ref_13","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). Imagenet classification with deep convolutional neural networks. Proceedings of the International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201323). Non-local neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-assisted Intervention (MICCAI), Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s11280-018-0556-3","article-title":"Multi-scale deep context convolutional neural networks for semantic segmentation","volume":"22","author":"Zhou","year":"2019","journal-title":"World Wide Web"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 16\u201320). Dual attention network for scene segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhang, H., Wang, C., and Xie, J. (2019, January 16\u201320). Co-occurrent features in semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00064"},{"key":"ref_23","unstructured":"Yuan, Y., and Wang, J. (2018). OCNet: Object context network for scene parsing. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Hu, X., Yang, K., Fei, L., and Wang, K. (2019, January 22\u201325). ACNET: Attention based network to exploit complementary features for rgbd semantic segmentation. Proceedings of the IEEE Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803025"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yang, K., Wang, K., Bergasa, L.M., Romera, E., Hu, W., Sun, D., Sun, J., Cheng, R., Chen, T., and L\u00f3pez, E. (2018). Unifying terrain awareness for the visually impaired through real-time semantic segmentation. Sensors, 18.","DOI":"10.3390\/s18051506"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, H., Dana, K., Shi, J., Zhang, Z., Wang, X., Tyagi, A., and Agrawal, A. (2018, January 18\u201323). Context encoding for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00747"},{"key":"ref_29","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (July, January 26). The cityscapes dataset for semantic urban scene understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional block attention module. Proceedings of the European Conference on Computer Vision, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., and Reid, I. (2017, January 21\u201326). Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., and Cottrell, G. (2018, January 12\u201315). Understanding convolution for semantic segmentation. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00163"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.patcog.2019.01.006","article-title":"Wider or deeper: Revisiting the resnet model for visual recognition","volume":"90","author":"Wu","year":"2019","journal-title":"Pattern Recog."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2907\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:30:57Z","timestamp":1760175057000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2907"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,21]]},"references-count":34,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20102907"],"URL":"https:\/\/doi.org\/10.3390\/s20102907","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,21]]}}}