{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T17:26:00Z","timestamp":1765905960076,"version":"3.48.0"},"reference-count":42,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T00:00:00Z","timestamp":1765843200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the advancement of deep learning, image semantic segmentation has achieved remarkable progress. However, the complexity and real-time requirements of practical applications pose greater challenges for segmentation algorithms. To address these, we propose a dual-branch network guided by attention mechanisms that tackles common limitations in existing methods, such as coarse edge segmentation, insufficient contextual understanding, and high computational overhead. Specifically, we introduce a superpixel sampling weighting module that models pixel dependencies based on different regional affiliations, thereby enhancing the network\u2019s sensitivity to object boundaries while preserving local features. Furthermore, a class-center attention module is designed to extract class-centered features and facilitate category-aware modeling. This module reduces the computational overhead and redundancy of traditional self-attention mechanisms, thereby improving the network\u2019s global feature representation. Additionally, learnable parameters are employed to adaptively fuse features from both branches, enabling the network to better focus on critical information. We validate our method on three benchmark datasets (PASCAL VOC 2012, Cityscapes, and ADE20K) by comparing it with mainstream models including FCN, DeepLabV3+, and DANet, with evaluation metrics of mIoU and PA. Our method delivers superior segmentation performance in these experiments. These results underscore the effectiveness of the proposed algorithm in balancing segmentation accuracy and model efficiency.<\/jats:p>","DOI":"10.3390\/s25247637","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T17:22:58Z","timestamp":1765905778000},"page":"7637","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual-Branch Superpixel and Class-Center Attention Network for Efficient Semantic Segmentation"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2041-4808","authenticated-orcid":false,"given":"Yunting","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Design Media, WuXi Vocational Institute of Commerce, Wuxi 214166, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2679-3654","authenticated-orcid":false,"given":"Hongbin","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haonan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengru","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3966-4691","authenticated-orcid":false,"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Central South University, Changsha 410017, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yeh-Cheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of California Davis, Davis, CA 95616, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Schonfeld, E., Schiele, B., and Khoreva, A. (2020, January 13\u201319). A u-net based discriminator for generative adversarial networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00823"},{"key":"ref_2","first-page":"171","article-title":"Retina u-net: Embarrassingly simple exploitation of segmentation supervision for medical object detection","volume":"116","author":"Jaeger","year":"2020","journal-title":"Proc. Mach. Learn. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1016\/j.neunet.2022.10.034","article-title":"Baseg: Boundary aware semantic segmentation for autonomous driving","volume":"157","author":"Xiao","year":"2023","journal-title":"Neural Netw."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3258","DOI":"10.1109\/TITS.2020.2980426","article-title":"Real-time high-performance semantic image segmentation of urban street scenes","volume":"22","author":"Dong","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3613","DOI":"10.1007\/s40747-021-00397-8","article-title":"Evaluation of deep learning algorithms for semantic segmentation of car parts","volume":"8","author":"Pasupa","year":"2022","journal-title":"Complex Intell. Syst."},{"key":"ref_7","first-page":"968","article-title":"A fast algorithm for two-dimensional Otsu adaptive threshold algorithm","volume":"33","author":"Haiyang","year":"2007","journal-title":"Acta Autom. Sin."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1109\/83.366472","article-title":"A new criterion for automatic multilevel thresholding","volume":"4","author":"Yen","year":"1995","journal-title":"IEEE Trans. Image Process."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Montavon, G., Orr, G., and M\u00fcller, K.-R. (2012). Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/978-3-642-35289-8"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/TIT.1975.1055330","article-title":"The estimation of the gradient of a density function, with applications in pattern recognition","volume":"21","author":"Fukunaga","year":"1975","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2274","DOI":"10.1109\/TPAMI.2012.120","article-title":"SLIC superpixels compared to state-of-the-art superpixel methods","volume":"34","author":"Achanta","year":"2012","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1006\/cviu.2001.0931","article-title":"Edge detector evaluation using empirical ROC curves","volume":"84","author":"Bowyer","year":"2001","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yang, L., Wu, X., Zhao, D., Li, H., and Zhai, J. (2011, January 15\u201317). An improved Prewitt algorithm for edge detection based on noised image. Proceedings of the 2011 4th International Congress on Image and Signal Processing, Shanghai, China.","DOI":"10.1109\/CISP.2011.6100495"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2289","DOI":"10.1109\/TIP.2009.2025560","article-title":"Image segmentation based on GrabCut framework integrating multiscale nonlinear structure tensor","volume":"18","author":"Han","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tang, M., Gorelick, L., Veksler, O., and Boykov, Y. (2013, January 1\u20138). GrabCut in one cut. Proceedings of the IEEE International Conference on Computer Vision, Sydney, NSW, Australia.","DOI":"10.1109\/ICCV.2013.222"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1145\/1015706.1015720","article-title":"\u201cGrabCut\u201d interactive foreground extraction using iterated graph cuts","volume":"23","author":"Rother","year":"2004","journal-title":"ACM Trans. Graph."},{"key":"ref_17","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_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","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_20","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_21","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. 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":"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_24","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhang, Y., Liu, S., Shi, J., Loy, C.C., Lin, D., and Jia, J. (2018, January 8\u201314). Psanet: Point-wise spatial attention network for scene parsing. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01240-3_17"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019, January 15\u201320). Dual attention network for scene segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_26","unstructured":"Huang, Z., Wang, X., Huang, L., Huang, C., Wei, Y., and Liu, W. (November, January 27). CCNet: Criss-cross attention for semantic segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Chen, X., and Wang, J. (2020, January 23\u201328). Object-contextual representations for semantic segmentation. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58539-6_11"},{"key":"ref_28","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_29","unstructured":"Li, Z., and Chen, J. (2015, January 7\u201312). Superpixel segmentation using linear spectral clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Achanta, R., and Susstrunk, S. (2017, January 21\u201326). Superpixels and polygons using simple non iterative clustering. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.520"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jampani, V., Sun, D., Liu, M.-Y., Yang, M.-H., and Kautz, J. (2018, January 8\u201314). Superpixel sampling networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_22"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Yang, F., Sun, Q., Jin, H., and Zhou, Z. (2020, January 13\u201319). Superpixel segmentation with fully convolutional networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01398"},{"key":"ref_33","unstructured":"Park, J., Woo, S., Lee, J.-Y., and Kweon, I.S. (2018). BAM: Bottleneck attention module. arXiv."},{"key":"ref_34","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 (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 8\u201314). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Munich, Germany.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, Q.-L., and Yang, Y.-B. (2021, January 6\u201311). SA-Net: Shuffle attention for deep convolutional neural networks. Proceedings of the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Toronto, ON, Canada.","DOI":"10.1109\/ICASSP39728.2021.9414568"},{"key":"ref_37","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_38","doi-asserted-by":"crossref","first-page":"115090","DOI":"10.1016\/j.eswa.2021.115090","article-title":"DSANet: Dilated spatial attention for real-time semantic segmentation in urban street scenes","volume":"183","author":"Elhassan","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1007\/s10489-021-02446-8","article-title":"Joint pyramid attention network for real-time semantic segmentation of urban scenes","volume":"52","author":"Hu","year":"2022","journal-title":"Appl. Intell."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ye, M., Ouyang, J., Chen, G., Zhang, J., and Yu, X. (2021, January 10\u201315). Enhanced feature pyramid network for semantic segmentation. Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9413224"},{"key":"ref_41","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_42","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"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/24\/7637\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T17:23:58Z","timestamp":1765905838000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/24\/7637"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,16]]},"references-count":42,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["s25247637"],"URL":"https:\/\/doi.org\/10.3390\/s25247637","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,16]]}}}