{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:05:59Z","timestamp":1760231159361,"version":"build-2065373602"},"reference-count":25,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,29]],"date-time":"2022-08-29T00:00:00Z","timestamp":1661731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EEG recognition","award":["61673079"],"award-info":[{"award-number":["61673079"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Instance segmentation has been developing rapidly in recent years. Mask R-CNN, a two-stage instance segmentation approach, has demonstrated exceptional performance. However, the masks are still very coarse. The downsampling operation of the backbone network and the ROIAlign layer loses much detailed information, especially from large targets. The sawtooth effect of the edge mask is caused by the lower resolution. A lesser percentage of boundary pixels leads to not-fine segmentation. In this paper, we propose a new method called Boundary Refine (BRefine) that achieves high-quality segmentation. This approach uses FCN as the foundation segmentation architecture, and forms a multistage fusion mask head with multistage fusion detail features to improve mask resolution. However, the FCN architecture causes inconsistencies in multiscale segmentation. BRank and sort loss (BR and S loss) is proposed to solve the problems of segmentation inconsistency and the difficulty of boundary segmentation. It is combined with rank and sort loss, and boundary region loss. BRefine can handle hard-to-partition boundaries and output high-quality masks. On the COCO, LVIS, and Cityscapes datasets, BRefine outperformed Mask R-CNN by 3.0, 4.2, and 3.5 AP, respectively. Furthermore, on the COCO dataset, the large objects improved by 5.0 AP.<\/jats:p>","DOI":"10.3390\/s22176499","type":"journal-article","created":{"date-parts":[[2022,8,30]],"date-time":"2022-08-30T01:37:55Z","timestamp":1661823475000},"page":"6499","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BRefine: Achieving High-Quality Instance Segmentation"],"prefix":"10.3390","volume":"22","author":[{"given":"Jimin","family":"Yu","sequence":"first","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiankun","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5057-8431","authenticated-orcid":false,"given":"Shangbo","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Computer Science, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shougang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shangguo","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, K., Pang, J., Wang, J., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Shi, J., and Ouyang, W. (2019, January 15\u201320). Hybrid task cascade for instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00511"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cheng, T., Wang, X., Huang, L., and Liu, W. (2020). Boundary-preserving mask r-cnn. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-58568-6_39"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Wu, Y., He, K., and Girshick, R. (2020, January 13\u201319). Pointrend: Image segmentation as rendering. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00982"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., and Chen, H. (2020, January 23\u201328). Conditional convolutions for instance segmentation. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_17"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, X., Kong, T., Shen, C., Jiang, Y., and Li, L. (2020, January 23\u201328). Solo: Segmenting objects by locations. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58523-5_38"},{"key":"ref_6","unstructured":"Bolya, D., Zhou, C., Xiao, F., and Lee, Y.J. (November, January 27). Yolact: Real-time instance segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, H., Sun, K., Tian, Z., Shen, C., Huang, Y., and Yan, Y. (2020, January 13\u201319). Blendmask: Top-down meets bottom-up for instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00860"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Liang, J., Homayounfar, N., Ma, W.C., Xiong, Y., Hu, R., and Urtasun, R. (2020, January 13\u201319). Polytransform: Deep polygon transformer for instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00915"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017, January 21\u201326). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_12","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_13","first-page":"1483","article-title":"Cascade R-CNN: High quality object detection and instance segmentation","volume":"43","author":"Zhaowei","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Huang, Z., Huang, L., Gong, Y., Huang, C., and Wang, X. (2019, January 15\u201320). Mask scoring r-cnn. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00657"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, J.J., Hou, Q., Cheng, M.M., Feng, J., and Jiang, J. (2019, January 15\u201320). A simple pooling-based design for real-time salient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00404"},{"key":"ref_16","unstructured":"Xu, D., Guan, J., Feng, P., and Wang, W. (2017, January 21\u201326). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Honolulu, HI, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-net: Fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Oksuz, K., Cam, B.C., Akbas, E., and Kalkan, S. (2021, January 11\u201317). Rank & sort loss for object detection and instance segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00300"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3782","DOI":"10.1109\/TPAMI.2020.2991457","article-title":"AP-loss for accurate one-stage object detection","volume":"43","author":"Chen","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","first-page":"15534","article-title":"A ranking-based, balanced loss function unifying classification and localisation in object detection","volume":"33","author":"Oksuz","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019, January 15\u201320). Generalized intersection over union: A metric and a loss for bounding box regression. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_22","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., and Xu, J. (2019). MMDetection: Open mmlab detection toolbox and benchmark. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollar, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gupta, A., Dollar, P., and Girshick, R. (2019, January 15\u201320). Lvis: A dataset for large vocabulary instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00550"},{"key":"ref_25","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, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6499\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:19:36Z","timestamp":1760141976000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/17\/6499"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,29]]},"references-count":25,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["s22176499"],"URL":"https:\/\/doi.org\/10.3390\/s22176499","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,8,29]]}}}