{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T12:30:57Z","timestamp":1783600257561,"version":"3.55.0"},"reference-count":49,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"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>In the industrial strip steel production process, complex background interference and varying defect scales make existing semantic segmentation models prone to scale confusion and noise propagation issues during the feature representation stage, thereby limiting segmentation accuracy and stability. To address these problems, this paper proposes a steel surface defect segmentation network based on dynamic receptive field modulation and semantic-guided adaptive modulated fusion, termed DAMF-Net. First, in the encoding stage, a kernel selection fusion attention module (KSFA) is designed, which constructs multi-scale receptive field responses through cascaded depthwise convolution, and combines spatial average prior with channel-level competitive weights to perform adaptive gating modulation on bottleneck features, thereby enhancing the model\u2019s ability to represent different scale defect structures; second, in the decoding stage, a task-oriented Adaptive Modulated Fusion Module (AMFM), inspired by modulation-based feature fusion, is introduced to adaptively fuse shallow detail features and deep semantic features through branch-wise competitive weighting for each channel, thereby reducing the propagation of shallow background noise during decoding; additionally, a hybrid optimization objective combining binary cross-entropy and Dice loss is constructed to enhance the model\u2019s learning ability for small-scale and low-contrast defects. Experimental results on the Severstal and ESDIs-SOD datasets show that the proposed method improves the mDice\/Dice metrics by 2.38% and 4.40% respectively compared to the U-Net baseline, and exhibits lower prediction errors and more stable segmentation performance under complex background conditions. Meanwhile, DAMF-Net achieves an inference speed of 55.8 FPS while ensuring improved accuracy, demonstrating a good balance between precision and efficiency. This method provides an effective solution for high-precision segmentation of steel surface defects in complex industrial scenarios.<\/jats:p>","DOI":"10.3390\/s26144360","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T11:55:32Z","timestamp":1783598132000},"page":"4360","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DAMF-Net: A Dynamic Receptive Field Enhancement and Semantic-Guided Adaptive Modulated Fusion Network for Steel Surface Defect Segmentation"],"prefix":"10.3390","volume":"26","author":[{"given":"Dengbiao","family":"Liu","sequence":"first","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Tai\u2019an 271019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhennan","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Tai\u2019an 271019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Tai\u2019an 271019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenxi","family":"Cui","sequence":"additional","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Tai\u2019an 271019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Tai\u2019an 271019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Boxing","family":"Yue","sequence":"additional","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Tai\u2019an 271019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linhai","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Tai\u2019an 271019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cuiyun","family":"Li","sequence":"additional","affiliation":[{"name":"College of Intelligent Equipment, Shandong University of Science and Technology, Tai\u2019an 271019, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109166","DOI":"10.1016\/j.compeleceng.2024.109166","article-title":"Depth Feature Fusion Based Surface Defect Region Identification Method for Steel Plate Manufacturing","volume":"116","author":"Bai","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3691","DOI":"10.1007\/s00170-025-16539-y","article-title":"Steel Surface Defects Analysis with Machine Vision and Deep Learning","volume":"140","author":"Frydrych","year":"2025","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4203","DOI":"10.1109\/TIA.2022.3151560","article-title":"Automated Surface Defect Detection in Metals: A Comparative Review of Object Detection and Semantic Segmentation Using Deep Learning","volume":"58","author":"Usamentiaga","year":"2022","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.jmsy.2025.11.022","article-title":"A Comprehensive Survey for Real-World Industrial Surface Defect Detection: Challenges, Approaches, and Prospects","volume":"84","author":"Cheng","year":"2026","journal-title":"J. Manuf. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109469","DOI":"10.1016\/j.engappai.2024.109469","article-title":"Two-Stage Encoder Multi-Decoder Network with Global\u2013Local up-Sampling for Defect Segmentation of Strip Steel Surface Defects","volume":"138","author":"Xu","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, J., Long, Z., Lee, M.-J., Feng, Y., E, J., Xu, Y., Zhang, Y., Alexander, I., Tang, X., and Fan, R. (2025). Edge-Aware and Deformable Feature Fusion for Steel Surface Defect Segmentation. Proceedings of the 2025 IEEE International Conference on Real-Time Computing and Robotics (RCAR), IEEE.","DOI":"10.1109\/RCAR65431.2025.11139516"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"886","DOI":"10.1007\/s11227-025-07397-2","article-title":"Lightweight Metal Surface Defect Segmentation Method Based on Multi-Scale Feature Fusion and Knowledge Distillation","volume":"81","author":"Li","year":"2025","journal-title":"J. Supercomput."},{"key":"ref_8","first-page":"5000313","article-title":"CDDNet: Camouflaged Defect Detection Network for Steel Surface","volume":"73","author":"Luo","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), IEEE.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_11","first-page":"5030013","article-title":"Context-Aware Adaptive Weighted Attention Network for Real-Time Surface Defect Segmentation","volume":"73","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_12","first-page":"1","article-title":"Fast-SDNet: An Extremely Lightweight and Accurate Surface Defect Segmentation Network","volume":"40","author":"Wang","year":"2025","journal-title":"Nondestruct. Test. Eval."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xiao, H., Ren, Z., and Cai, S. (2025). HMNet: High-Resolution Mamba Network for Semantic Segmentation of Surface Defects. Proceedings of the 2025 28th International Conference on Computer Supported Cooperative Work in Design (CSCWD), IEEE.","DOI":"10.1109\/CSCWD64889.2025.11033237"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Sun, Y., Geng, S., Zheng, C., Xu, C., Guo, H., and Feng, Y. (2025). DEPANet: A Differentiable Edge-Guided Pyramid Aggregation Network for Strip Steel Surface Defect Segmentation. Algorithms, 18.","DOI":"10.3390\/a18050279"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"131314","DOI":"10.1016\/j.eswa.2026.131314","article-title":"LACTNet: A Label-Aware CNN-Transformer Network for Surface Defect Segmentation","volume":"310","author":"Ameri","year":"2026","journal-title":"Expert Syst. Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Albanie, S., Sun, G., and Wu, E. (2019). Squeeze-and-Excitation Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., and Kweon, I.S. (2018). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Hu, X., and Yang, J. (2019). Selective Kernel Networks. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2019.00060"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., and Barnard, K. (2021). Attentional Feature Fusion. Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), IEEE.","DOI":"10.1109\/WACV48630.2021.00360"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Yan, F., Jiang, X., Lu, Y., Cao, J., Chen, D., and Xu, M. (2025). Wavelet and Prototype Augmented Query-Based Transformer for Pixel-Level Surface Defect Detection. Proceedings of the Computer Vision and Pattern Recognition Conference, IEEE.","DOI":"10.1109\/CVPR52734.2025.02222"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"29198","DOI":"10.1038\/s41598-025-14935-8","article-title":"DSAT: A Dynamic Sparse Attention Transformer for Steel Surface Defect Detection with Hierarchical Feature Fusion","volume":"15","author":"Wu","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.1007\/s10845-025-02613-5","article-title":"AMFNet: Aggregated Multi-Level Feature Interaction Fusion Network for Defect Detection on Steel Surfaces","volume":"37","author":"Wei","year":"2026","journal-title":"J. Intell. Manuf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"103972","DOI":"10.1016\/j.rineng.2025.103972","article-title":"Steel Surface Defect Detection and Segmentation Using Deep Neural Networks","volume":"25","author":"Ashrafi","year":"2025","journal-title":"Results Eng."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhao, Z., Liu, Y., Liu, J., Ma, T., Wu, K., Zhuang, Z., and Wang, J. (2025). Steel Surface Defect Segmentation with SME-DeeplabV3+. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0329628"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"055118","DOI":"10.1063\/5.0274903","article-title":"A High-Precision Segmentation Network for Industrial Surface Defect Detection","volume":"15","author":"Chen","year":"2025","journal-title":"AIP Adv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"114346","DOI":"10.1016\/j.asoc.2025.114346","article-title":"PASS-Net: Parameter-Adaptive State-Space Network for Real-Time and High-Accuracy Segmentation of Industrial Surface Defects","volume":"187","author":"Kong","year":"2026","journal-title":"Appl. Soft Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e70187","DOI":"10.1111\/exsy.70187","article-title":"DSGNet: A Lightweight Network Integrating Depthwise Separable and Ghost Convolutions for Real-time Surface Defect Segmentation","volume":"43","author":"Lu","year":"2026","journal-title":"Expert Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, J., Ding, R., Ban, M., and Guo, T. (2022). FDSNeT: An Accurate Real-Time Surface Defect Segmentation Network. Proceedings of the ICASSP 2022\u20142022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE.","DOI":"10.1109\/ICASSP43922.2022.9747311"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Xu, J., Xiong, Z., and Bhattacharyya, S.P. (2023). PIDNet: A Real-Time Semantic Segmentation Network Inspired by PID Controllers. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR52729.2023.01871"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"103031","DOI":"10.52202\/079017-3273","article-title":"VMamba: Visual State Space Model","volume":"37","author":"Liu","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"110211","DOI":"10.1016\/j.measurement.2021.110211","article-title":"DCC-CenterNet: A Rapid Detection Method for Steel Surface Defects","volume":"187","author":"Tian","year":"2022","journal-title":"Measurement"},{"key":"ref_33","first-page":"5005610","article-title":"A Real-Time Steel Surface Defect Detection Approach with High Accuracy","volume":"71","author":"Wang","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1256","DOI":"10.1109\/LSP.2025.3546888","article-title":"GLNet: Global-Local Fusion Network for Strip Steel Surface Defects Detection","volume":"32","author":"Wang","year":"2025","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"107404","DOI":"10.1016\/j.patcog.2020.107404","article-title":"U2-Net: Going Deeper with Nested U-Structure for Salient Object Detection","volume":"106","author":"Qin","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"107311","DOI":"10.1016\/j.neunet.2025.107311","article-title":"Dual Selective Fusion Transformer Network for Hyperspectral Image Classification","volume":"187","author":"Xu","year":"2025","journal-title":"Neural Netw."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhou, S., and Li, H. (2024). Depth Information Assisted Collaborative Mutual Promotion Network for Single Image Dehazing. Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52733.2024.00275"},{"key":"ref_40","unstructured":"(2025, August 27). Severstal Steel Defect Detection. Available online: https:\/\/www.Kaggle.Com\/c\/Severstal-Steel-Defect-Detection."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5019412","DOI":"10.1109\/TIM.2023.3290965","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_42","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017). Pyramid Scene Parsing Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","article-title":"Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation","volume":"Volume 11211","author":"Ferrari","year":"2018","journal-title":"Computer Vision\u2014ECCV 2018"},{"key":"ref_44","unstructured":"Li, H., Xiong, P., An, J., and Wang, L. (2018). Pyramid Attention Network for Semantic Segmentation. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (2019). Dual Attention Network for Scene Segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR.2019.00326"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"179656","DOI":"10.1109\/ACCESS.2020.3025372","article-title":"MA-Net: A Multi-Scale Attention Network for Liver and Tumor Segmentation","volume":"8","author":"Fan","year":"2020","journal-title":"IEEE Access"},{"key":"ref_47","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_48","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and Sang, N. (2018). BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation. Proceedings of the European Conference on Computer Vision (ECCV 2018), IEEE.","DOI":"10.1007\/978-3-030-01261-8_20"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"10805","DOI":"10.1007\/s00371-025-04070-w","article-title":"Efficient Hierarchical Multiscale Convolutional Attention for Accurate Medical Image Segmentation","volume":"41","author":"Wang","year":"2025","journal-title":"Vis. 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