{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T14:52:53Z","timestamp":1740149573986,"version":"3.37.3"},"reference-count":30,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T00:00:00Z","timestamp":1678752000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China","award":["2020AAA0109701"]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62076024","62006018"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Interdisciplinary Research Project for Young Teachers of USTB","award":["FRF-IDRY-21-018"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"Recently, semantic segmentation has been widely applied in various realistic scenarios. Many semantic segmentation backbone networks use various forms of dense connection to improve the efficiency of gradient propagation in the network. They achieve excellent segmentation accuracy but lack inference speed. Therefore, we propose a backbone network SCDNet with a dual path structure and higher speed and accuracy. Firstly, we propose a split connection structure, which is a streamlined lightweight backbone with a parallel structure to increase inference speed. Secondly, we introduce a flexible dilated convolution using different dilation rates so that the network can have richer receptive fields to perceive objects. Then, we propose a three-level hierarchical module to effectively balance the feature maps with multiple resolutions. Finally, a refined flexible and lightweight decoder is utilized. Our work achieves a trade-off of accuracy and speed on the Cityscapes and Camvid datasets. Specifically, we obtain a 36% improvement in FPS and a 0.7% improvement in mIoU on the Cityscapes test set.<\/jats:p>","DOI":"10.3390\/s23063112","type":"journal-article","created":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T10:57:11Z","timestamp":1678791431000},"page":"3112","source":"Crossref","is-referenced-by-count":5,"title":["Faster SCDNet: Real-Time Semantic Segmentation Network with Split Connection and Flexible Dilated Convolution"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4910-6071","authenticated-orcid":false,"given":"Shu","family":"Tian","sequence":"first","affiliation":[{"name":"School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Guangyu","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]},{"given":"Songlu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer & Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,14]]},"reference":[{"key":"ref_1","unstructured":"Chao, P., Kao, C.Y., Ruan, Y.S., Huang, C.H., and Lin, Y.L. (November, January 27). Hardnet: A low memory traffic network. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_2","unstructured":"Tao, A., Sapra, K., and Catanzaro, B. (2020). Hierarchical multi-scale attention for semantic segmentation. arXiv."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 15\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_4","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":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and Sang, N. (2018, January 8\u201314). Bisenet: Bilateral segmentation network for real-time semantic segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3051","DOI":"10.1007\/s11263-021-01515-2","article-title":"Bisenet v2: Bilateral network with guided aggregation for real-time semantic segmentation","volume":"129","author":"Yu","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_8","unstructured":"Chen, W., Gong, X., Liu, X., Zhang, Q., Li, Y., and Wang, Z. (2019). Fasterseg: Searching for faster real-time semantic segmentation. arXiv."},{"key":"ref_9","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2016). Enet: A deep neural network architecture for real-time semantic segmentation. arXiv."},{"key":"ref_10","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_11","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_12","doi-asserted-by":"crossref","unstructured":"Fan, M., Lai, S., Huang, J., Wei, X., Chai, Z., Luo, J., and Wei, X. (2021, January 20\u201325). Rethinking BiSeNet for real-time semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00959"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhu, L., Deng, R., Maire, M., Deng, Z., Mori, G., and Tan, P. (2018, January 8\u201314). Sparsely aggregated convolutional networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01258-8_12"},{"key":"ref_14","unstructured":"Iandola, F., Moskewicz, M., Karayev, S., Girshick, R., Darrell, T., and Keutzer, K. (2014). Densenet: Implementing efficient convnet descriptor pyramids. arXiv."},{"key":"ref_15","unstructured":"Peng, J., Liu, Y., Tang, S., Hao, Y., Chu, L., Chen, G., Wu, Z., Chen, Z., Yu, Z., and Du, Y. (2022). PP-LiteSeg: A Superior Real-Time Semantic Segmentation Model. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Lai, X., Tian, Z., Xu, X., Chen, Y.C., Liu, S., Zhao, H., Wang, L., and Jia, J. (2022, January 23\u201327). DecoupleNet: Decoupled Network for Domain Adaptive Semantic Segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19827-4_22"},{"key":"ref_17","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_18","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, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liu, C., Chen, L.C., Schroff, F., Adam, H., Hua, W., Yuille, A.L., and Fei-Fei, L. (2019, January 15\u201320). Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00017"},{"key":"ref_20","unstructured":"Gao, R. (2021). Rethink dilated convolution for real-time semantic segmentation. arXiv."},{"key":"ref_21","unstructured":"Hong, Y., Pan, H., Sun, W., and Jia, Y. (2021). Deep dual-resolution networks for real-time and accurate semantic segmentation of road scenes. arXiv."},{"key":"ref_22","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 (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., and Schiele, B. (2016, January 27\u201330). The Cityscapes Dataset for Semantic Urban Scene Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_24","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.patrec.2008.04.005","article-title":"Semantic object classes in video: A high-definition ground truth database","volume":"30","author":"Brostow","year":"2009","journal-title":"Pattern Recognit. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Qiu, Z., Liu, J., Yao, T., Liu, D., and Mei, T. (2019, January 15\u201320). Customizable architecture search for semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01191"},{"key":"ref_27","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, X., You, A., Zhu, Z., Zhao, H., Yang, M., Yang, K., Tan, S., and Tong, Y. (2020, January 23\u201328). Semantic flow for fast and accurate scene parsing. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_45"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"118537","DOI":"10.1016\/j.eswa.2022.118537","article-title":"CSRNet: Cascaded Selective Resolution Network for real-time semantic segmentation","volume":"211","author":"Xiong","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lu, Z., Cheng, R., Huang, S., Zhang, H., Qiu, C., and Yang, F. (2022). Surrogate-assisted Multiobjective Neural Architecture Search for Real-time Semantic Segmentation. IEEE Trans. Artif. 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