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Education","award":["25B520004"],"award-info":[{"award-number":["25B520004"]}]},{"name":"Open Fund of the Engineering Research Center of Intelligent Swarm Systems, Ministry of Education","award":["ZZU-CISS-2024004"],"award-info":[{"award-number":["ZZU-CISS-2024004"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>The steel strip is an important and ideal material for the automotive and aerospace industries due to its superior machinability, cost efficiency, and flexibility. However, surface defects such as inclusions, spots, and scratches can significantly impact product performance and durability. Accurately identifying these defects remains challenging due to the complex texture structures and subtle variations in the material. In order to tackle this challenge, we propose a Differentiable Edge-guided Pyramid Aggregation Network (DEPANet) to utilize edge information for improving segmentation performance. DEPANet adopts an end-to-end encoder-decoder framework, where the encoder consisting of three key components: a backbone network, a Differentiable Edge Feature Pyramid network (DEFP), and Edge-aware Feature Aggregation Modules (EFAMs). The backbone network is designed to extract overall features from the strip steel surface, while the proposed DEFP utilizes learnable Laplacian operators to extract multiscale edge information of defects across scales. In addition, the proposed EFAMs aggregate the overall features generating from the backbone and the edge information obtained from DEFP using the Convolutional Block Attention Module (CBAM), which combines channel attention and spatial attention mechanisms, to enhance feature expression. Finally, through the decoder, implemented as a Feature Pyramid Network (FPN), the multiscale edge-enhanced features are progressively upsampled and fused to reconstruct high-resolution segmentation maps, enabling precise defect localization and robust handling of defects across various sizes and shapes. DEPANet demonstrates superior segmentation accuracy, edge preservation, and feature representation on the SD-saliency-900 dataset, outperforming other state-of-the-art methods and delivering more precise and reliable defect segmentation.<\/jats:p>","DOI":"10.3390\/a18050279","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T06:18:51Z","timestamp":1746771531000},"page":"279","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["DEPANet: A Differentiable Edge-Guided Pyramid Aggregation Network for Strip Steel Surface Defect Segmentation"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3305-4622","authenticated-orcid":false,"given":"Yange","family":"Sun","sequence":"first","affiliation":[{"name":"School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siyu","family":"Geng","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengyi","family":"Zheng","sequence":"additional","affiliation":[{"name":"Information Center, Xinyang Agricultural and Forestry University, Xinyang 464000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenglong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaping","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"ref_1","first-page":"308","article-title":"Surface Defect Detection Algorithm for Strip Steel Based on Improved YOLOv7 Model","volume":"51","author":"Wang","year":"2024","journal-title":"IAENG Int. J. Comput. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ding, L., Xiao, L., Liao, B., Lu, R., and Peng, H. (2017). An improved recurrent neural network for complex-valued systems of linear equation and its application to robotic motion tracking. Front. Neurorobotics, 11.","DOI":"10.3389\/fnbot.2017.00045"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1007\/s11277-015-2934-9","article-title":"Multi-function current differencing cascaded transconductance amplifier (MCDCTA) and its application to current-mode multiphase sinusoidal oscillator","volume":"86","author":"Jin","year":"2016","journal-title":"Wirel. Pers. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"107122","DOI":"10.1016\/j.neunet.2025.107122","article-title":"VPT: Video portraits transformer for realistic talking face generation","volume":"184","author":"Zhang","year":"2025","journal-title":"Neural Netw."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2798","DOI":"10.1109\/JBHI.2020.3019505","article-title":"Adaptive feature selection guided deep forest for COVID-19 classification with chest CT","volume":"24","author":"Sun","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"115438","DOI":"10.1016\/j.measurement.2024.115438","article-title":"TSEDNet:Task-specific encoder\u2013decoder network for surface defects of strip steel","volume":"239","author":"Guo","year":"2025","journal-title":"Measurement"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"112467","DOI":"10.1016\/j.measurement.2023.112467","article-title":"MSC-DNet: An efficient detector with multi-scale context for defect detection on strip steel surface","volume":"209","author":"Liu","year":"2023","journal-title":"Measurement"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Du, Y., Chen, H., Fu, Y., Zhu, J., and Zeng, H. (2024). AFF-Net: A strip steel surface defect detection network via adaptive focusing features. IEEE Trans. Instrum. Meas., 73.","DOI":"10.1109\/TIM.2024.3398131"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"016222","DOI":"10.1088\/1361-6501\/ad9856","article-title":"Edge-aware interactive refinement network for strip steel surface defects detection","volume":"36","author":"Dong","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"165458","DOI":"10.1109\/ACCESS.2024.3494250","article-title":"Research on a Multiscale U-Net Lung Nodule Segmentation Model Based on Edge Perception and 3D Attention Mechanism Improvement","volume":"12","author":"Hui","year":"2024","journal-title":"IEEE Access"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"67","DOI":"10.2355\/isijinternational.ISIJINT-2023-222","article-title":"Resformer-Unet: A U-shaped Framework Combining ResNet and Transformer for Segmentation of Strip Steel Surface Defects","volume":"64","author":"Lu","year":"2024","journal-title":"ISIJ Int."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2094","DOI":"10.1109\/JSTARS.2023.3238720","article-title":"ResAt-UNet: A U-shaped network using ResNet and attention module for image segmentation of urban buildings","volume":"16","author":"Fan","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_14","first-page":"27","article-title":"ResNet-SVM: Fusion based glioblastoma tumor segmentation and classification","volume":"31","author":"Sahli","year":"2023","journal-title":"J. X-Ray Sci. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Abdelrahman, A., and Viriri, S. (2023). FPN-SE-ResNet model for accurate diagnosis of kidney tumors using CT images. Appl. Sci., 13.","DOI":"10.3390\/app13179802"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"108623","DOI":"10.1016\/j.compag.2024.108623","article-title":"FRPNet: An improved Faster-ResNet with PASPP for real-time semantic segmentation in the unstructured field scene","volume":"217","author":"Yang","year":"2024","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yin, T., Chen, X., Hauwa, A.S., Deng, B., Zhu, Y., Gao, S., Zang, H., and Zhao, H. (2024). A steel defect detection method based on edge feature extraction via the Sobel operator. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-79205-5"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106339","DOI":"10.1016\/j.neunet.2024.106339","article-title":"DCDLN: A densely connected convolutional dynamic learning network for malaria disease diagnosis","volume":"176","author":"Zhang","year":"2024","journal-title":"Neural Netw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"118833","DOI":"10.1016\/j.eswa.2022.118833","article-title":"Edge U-Net: Brain tumor segmentation using MRI based on deep U-Net model with boundary information","volume":"213","author":"Allah","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"3263","DOI":"10.1109\/TBME.2024.3415818","article-title":"An Efficient Muscle Segmentation Method via Bayesian Fusion of Probabilistic Shape Modeling and Deep Edge Detection","volume":"71","author":"Wang","year":"2024","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_22","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_23","unstructured":"Gedraite, E.S., and Hadad, M. (2011, January 14\u201316). Investigation on the Effect of a Gaussian Blur in Image Filtering and Segmentation. Proceedings of the 53rd International Symposium ELMAR-2011, Zadar, Croatia."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"106621","DOI":"10.1016\/j.neunet.2024.106621","article-title":"Joint computation offloading and resource allocation for end-edge collaboration in internet of vehicles via multi-agent reinforcement learning","volume":"179","author":"Wang","year":"2024","journal-title":"Neural Netw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5393","DOI":"10.30534\/ijatcse\/2020\/175942020","article-title":"Binary cross entropy with deep learning technique for image classification","volume":"9","author":"Ruby","year":"2020","journal-title":"Int. J. Adv. Trends Comput. Sci. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, X., Sun, X., Meng, Y., Liang, J., Wu, F., and Li, J. (2019). Dice loss for data-imbalanced NLP tasks. arXiv.","DOI":"10.18653\/v1\/2020.acl-main.45"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"106000","DOI":"10.1016\/j.optlaseng.2019.106000","article-title":"Saliency Detection for Strip Steel Surface Defects Using Multiple Constraints and Improved Texture Features","volume":"128","author":"Song","year":"2020","journal-title":"Opt. Lasers Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Kr\u00e4henb\u00fchl, P., Pritch, Y., and Hornung, A. (2012, January 16\u201321). Saliency Filters: Contrast-Based Filtering for Salient Region Detection. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6247743"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2622","DOI":"10.1007\/s11263-021-01490-8","article-title":"Structure-Measure: A New Way to Evaluate Foreground Maps","volume":"129","author":"Cheng","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Achanta, R., Hemami, S., Estrada, F., and Susstrunk, S. (2009, January 20\u201325). Frequency-Tuned Salient Region Detection. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPRW.2009.5206596"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fan, D.P., Gong, C., Cao, Y., Ren, B., Cheng, M.M., and Borji, A. (2018). Enhanced-Alignment Measure for Binary Foreground Map Evaluation. arXiv.","DOI":"10.24963\/ijcai.2018\/97"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5882","DOI":"10.1109\/TIP.2017.2738839","article-title":"Salient Region Detection Using Diffusion Process on a Two-Layer Sparse Graph","volume":"26","author":"Zhou","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Qin, X., Zhang, Z., Huang, C., Gao, C., Dehghan, M., and Jagersand, M. (2019, January 15\u201320). BASNet: Boundary-Aware Salient Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00766"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhu, W., Liang, S., Wei, Y., and Sun, J. (2014, January 23\u201328). Saliency Optimization from Robust Background Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.360"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, L., Dai, J., Lu, H., He, Y., and Wang, G. (2018, January 18\u201322). A Bi-Directional Message Passing Model for Salient Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00187"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Wu, Z., Su, L., and Huang, Q. (2019, January 15\u201320). Cascaded Partial Decoder for Fast and Accurate Salient Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00403"},{"key":"ref_37","first-page":"1","article-title":"Dense Attention-Guided Cascaded Network for Salient Object Detection of Strip Steel Surface Defects","volume":"71","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hou, Q., Cheng, M.M., Hu, X., Borji, A., Tu, Z., and Torr, P.H. (2017, January 21\u201326). Deeply Supervised Salient Object Detection with Short Connections. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.563"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"9709","DOI":"10.1109\/TIM.2020.3002277","article-title":"EDRNet: Encoder-Decoder Residual Network for Salient Object Detection of Strip Steel Surface Defects","volume":"69","author":"Song","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_40","unstructured":"Wei, J., Wang, S., and Huang, Q. (2020, January 7\u201312). F3Net:Fusion, Feedback, and Focus for Salient Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhou, H., Xie, X., Lai, J.H., Chen, Z., and Yang, L. (2020, January 14\u201319). Interactive Two-Stream Decoder for Accurate and Fast Saliency Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00916"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1911","DOI":"10.1109\/TIP.2017.2669878","article-title":"Salient Object Detection Via Multiple Instance Learning","volume":"26","author":"Huang","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Pang, Y., Zhao, X., Zhang, L., and Lu, H. (2020, January 14\u201319). Multi-Scale Interactive Network for Salient Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00943"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Luo, Z., Mishra, A., Achkar, A., Eichel, J., Li, S., and Jodoin, P.M. (2017, January 21\u201326). Non-Local Deep Features for Salient Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.698"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhao, T., and Wu, X. (2019, January 15\u201320). Pyramid Feature Attention Network for Saliency Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00320"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Liu, N., Han, J., and Yang, M.H. (2018, January 18\u201322). PiCANet: Learning Pixel-Wise Contextual Attention for Saliency Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00326"},{"key":"ref_47","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_48","doi-asserted-by":"crossref","unstructured":"Deng, Z., Hu, X., Zhu, L., Xu, X., Qin, J., Han, G., and Heng, P.A. (2018, January 13\u201319). R3Net: Recurrent Residual Refinement Network for Saliency Detection. Proceedings of the 27th International Joint Conference on Artificial Intelligence, Stockholm, Sweden.","DOI":"10.24963\/ijcai.2018\/95"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1109\/TIP.2017.2762422","article-title":"Reversion Correction and Regularized Random Walk Ranking for Saliency Detection","volume":"27","author":"Yuan","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"3804","DOI":"10.1109\/TIP.2021.3065239","article-title":"SAMNet: Stereoscopically Attentive Multi-Scale Network for Lightweight Salient Object Detection","volume":"30","author":"Liu","year":"2021","journal-title":"IEEE Trans. Image Process."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TPAMI.2016.2562626","article-title":"Salient Object Detection Via Structured Matrix Decomposition","volume":"39","author":"Peng","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_52","first-page":"1","article-title":"Autocorrelation-aware aggregation network for salient object detection of strip steel surface defects","volume":"72","author":"Cui","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Sun, Y., Chen, G., Zhou, T., Zhang, Y., and Liu, N. (2021). Context-aware cross-level fusion network for camouflaged object detection. arXiv.","DOI":"10.24963\/ijcai.2021\/142"},{"key":"ref_54","first-page":"5617712","article-title":"Lightweight salient object detection in optical remote sensing images via feature correlation","volume":"60","author":"GongyangLi","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/5\/279\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:29:55Z","timestamp":1760030995000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/5\/279"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,9]]},"references-count":54,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["a18050279"],"URL":"https:\/\/doi.org\/10.3390\/a18050279","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,9]]}}}