{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T18:00:02Z","timestamp":1765389602866,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Provincial Science and Technology Innovation Special Fund Project of Jilin Province","award":["20190302026GX","20200201037JC"],"award-info":[{"award-number":["20190302026GX","20200201037JC"]}]},{"DOI":"10.13039\/100007847","name":"Natural Science Foundation of Jilin Province","doi-asserted-by":"publisher","award":["20190302026GX","20200201037JC"],"award-info":[{"award-number":["20190302026GX","20200201037JC"]}],"id":[{"id":"10.13039\/100007847","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities for JLU","award":["20190302026GX","20200201037JC"],"award-info":[{"award-number":["20190302026GX","20200201037JC"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>In recent years, panoptic segmentation has garnered increasing attention from researchers aiming to better understand scenes in images. Although many excellent studies have been proposed, they share some common unresolved issues. Firstly, panoptic segmentation, as a novel task, is still confined within inherent frameworks. Secondly, the prevalent kernel update strategies do not adequately utilize the information from each stage. To address these two issues, redwe propose an edge-guided stepwise dual kernel update network (EGSDK-Net) for panoptic segmentation; the core components are the real-time edge guidance module and the stepwise dual kernel update module. The first component, after extracting and positioning edge features through an extra branch, applies these features to the normally transmitted feature maps within the network to highlight the edges. The input image is initially processed with the Canny edge detector to generate and store the predicted edge map, which acts as the ground truth for supervising the extracted edge feature map. The stepwise dual kernel update module enhances the utilization of information by allowing each stage to update both its own kernel and that of the subsequent stage, thereby improving the judgment capabilities of the kernels. redEGSDK-Net achieves a PQ of 60.6, representing a 2.19% improvement over RT-K-Net.<\/jats:p>","DOI":"10.3390\/a18020071","type":"journal-article","created":{"date-parts":[[2025,2,3]],"date-time":"2025-02-03T12:18:56Z","timestamp":1738585136000},"page":"71","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["EGSDK-Net: Edge-Guided Stepwise Dual Kernel Update Network for Panoptic Segmentation"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8333-3522","authenticated-orcid":false,"given":"Pengyu","family":"Mu","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Jilin University, Changchun 130012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kirillov, A., He, K., Girshick, R., Rother, C., and Doll\u00e1r, P. (2019, January 15\u201320). Panoptic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00963"},{"key":"ref_2","first-page":"10326","article-title":"K-net: Towards unified image segmentation","volume":"34","author":"Zhang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cheng, B., Misra, I., Schwing, A.G., Kirillov, A., and Girdhar, R. (2022, January 18\u201324). Masked-attention mask transformer for universal image segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00135"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yu, Q., Wang, H., Qiao, S., Collins, M., Zhu, Y., Adam, H., Yuille, A., and Chen, L.C. (2022, January 23\u201327). k-means Mask Transformer. Proceedings of the European Conference on Computer Vision, Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-19818-2_17"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Sch\u00f6n, M., Buchholz, M., and Dietmayer, K. (2023, January 4\u20137). Rt-k-net: Revisiting k-net for real-time panoptic segmentation. Proceedings of the 2023 IEEE Intelligent Vehicles Symposium (IV), Anchorage, AK, USA.","DOI":"10.1109\/IV55152.2023.10186625"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Hu, J., Huang, L., Ren, T., Zhang, S., Ji, R., and Cao, L. (2023, January 17\u201324). You only segment once: Towards real-time panoptic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2023, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01709"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"\u0160ari\u0107, J., Or\u0161i\u0107, M., and \u0160egvi\u0107, S. (2023). Panoptic SwiftNet: Pyramidal Fusion for Real-Time Panoptic Segmentation. Remote Sens., 15.","DOI":"10.3390\/rs15081968"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Wang, F., Wang, Z., Chen, Z., Zhu, D., Gong, X., and Cong, W. (2023). An edge-guided deep learning solar panel hotspot thermal image segmentation algorithm. Appl. Sci., 13.","DOI":"10.3390\/app131911031"},{"key":"ref_9","first-page":"1","article-title":"Edge detection guide network for semantic segmentation of remote-sensing images","volume":"20","author":"Jin","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Sch\u00f6n, M., Buchholz, M., and Dietmayer, K. (2021, January 11\u201317). Mgnet: Monocular geometric scene understanding for autonomous driving. Proceedings of the IEEE\/CVF International Conference on Computer Vision 2021, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.01551"},{"key":"ref_11","unstructured":"Chen, L.C., Wang, H., and Qiao, S. (2020). Scaling wide residual networks for panoptic segmentation. arXiv."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Petrovai, A., and Nedevschi, S. (November, January 19). Real-time panoptic segmentation with prototype masks for automated driving. Proceedings of the 2020 IEEE Intelligent Vehicles Symposium (IV), Las Vegas, NV, USA.","DOI":"10.1109\/IV47402.2020.9304836"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hou, R., Li, J., Bhargava, A., Raventos, A., Guizilini, V., Fang, C., Lynch, J., and Gaidon, A. (2020, January 14\u201319). Real-time panoptic segmentation from dense detections. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2020, Virtual.","DOI":"10.1109\/CVPR42600.2020.00855"},{"key":"ref_14","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 2016, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.350"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.1007\/s11263-021-01445-z","article-title":"Efficientps: Efficient panoptic segmentation","volume":"129","author":"Mohan","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Porzi, L., Bulo, S.R., Colovic, A., and Kontschieder, P. (2019, January 15\u201320). Seamless scene segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00847"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Gao, N., Shan, Y., Wang, Y., Zhao, X., Yu, Y., Yang, M., and Huang, K. (November, January 27). Ssap: Single-shot instance segmentation with affinity pyramid. Proceedings of the IEEE\/CVF International Conference on Computer Vision 2019, Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00073"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Cheng, B., Collins, M.D., Zhu, Y., Liu, T., Huang, T.S., Adam, H., and Chen, L.C. (2020, January 13\u201319). Panoptic-deeplab: A simple, strong, and fast baseline for bottom-up panoptic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2020, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01249"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Yu, Q., Wang, H., Kim, D., Qiao, S., Collins, M., Zhu, Y., Adam, H., Yuille, A., and Chen, L.C. (2022, January 18\u201324). Cmt-deeplab: Clustering mask transformers for panoptic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2022, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00259"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1109\/TCYB.2022.3163152","article-title":"Edge-Guided Recurrent Positioning Network for Salient Object Detection in Optical Remote Sensing Images","volume":"53","author":"Zhou","year":"2023","journal-title":"IEEE Trans. Cybern."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"063032","DOI":"10.1117\/1.JEI.32.6.063032","article-title":"EDGE-Net: An edge-guided enhanced network for RGB-T salient object detection","volume":"32","author":"Zheng","year":"2023","journal-title":"J. Electron. Imaging"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3956","DOI":"10.1109\/TNNLS.2020.3016321","article-title":"Multilevel Edge Features Guided Network for Image Denoising","volume":"32","author":"Fang","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_23","unstructured":"Wang, D., Xie, C., Liu, S., Niu, Z., and Zuo, W. (2021). Image inpainting with edge-guided learnable bidirectional attention maps. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Lin, H., Pagnucco, M., and Song, Y. (2021, January 20\u201325). Edge guided progressively generative image outpainting. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2021, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00090"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"108019","DOI":"10.1016\/j.patcog.2021.108019","article-title":"Edge-guided composition network for image stitching","volume":"118","author":"Dai","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","volume":"8","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","first-page":"7423","article-title":"RTFormer: Efficient design for real-time semantic segmentation with transformer","volume":"35","author":"Wang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_28","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_29","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) 2018, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_30","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems, Curran Associates, Inc."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201326). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/2\/71\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:25:16Z","timestamp":1760027116000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/2\/71"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,1]]},"references-count":31,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["a18020071"],"URL":"https:\/\/doi.org\/10.3390\/a18020071","relation":{},"ISSN":["1999-4893"],"issn-type":[{"type":"electronic","value":"1999-4893"}],"subject":[],"published":{"date-parts":[[2025,2,1]]}}}