{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T23:04:15Z","timestamp":1793142255860,"version":"4.5.0"},"reference-count":32,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Changzhou Science and Technology Project","award":["CQ20240062"],"award-info":[{"award-number":["CQ20240062"]}]}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>Steel manufacturing is a typical high-energy and high-carbon industry. Undetected tiny surface defects such as scratches and crazing lead to massive waste of steel raw materials and extra energy consumption in subsequent processing, while heavy detection models may increase the computational energy demand of on-site inspection systems. The purpose of this study is to develop a lightweight defect detection model that improves the recognition of weak-semantic and structurally distinctive surface defects while reducing computational cost for industrial deployment. To achieve this objective, we propose SA-DETR, a lightweight structure-aware defect detection model built on RT-DETR-L. Three targeted modules are introduced: O-EHGBlock retains fine-grained structural cues of micro-defects with reduced computation; Adaptive Structure-aware Query Selection (ASQS) supplements semantic query ranking with local structural information to improve the detection of weak-semantic defects; and lightweight StarC3 replaces redundant fusion blocks to cut computational overhead. Evaluated on NEU-DET and PCB datasets, SA-DETR boosts mAP@0.5 by 4.5 percentage points over RT-DETR-L, while reducing parameters by 36.0% and GFLOPs by 46.5%, with inference speed lifted to 103.0 FPS. These results demonstrate that SA-DETR provides a favorable trade-off between detection accuracy and computational efficiency, indicating its potential for resource-constrained industrial surface inspection and sustainable steel quality control.<\/jats:p>","DOI":"10.3390\/s26196075","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T08:09:02Z","timestamp":1790323742000},"page":"6075","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["SA-DETR: Lightweight Structure-Aware Defect Detection Model for Sustainable Low-Carbon Steel Manufacturing Based on RT-DETR-L"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8075-5996","authenticated-orcid":false,"given":"Peifeng","family":"Liang","sequence":"first","affiliation":[{"name":"School of Computer Engineering, Jiangsu University of Technology, Changzhou 213000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyue","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Jiangsu University of Technology, Changzhou 213000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1016\/j.procs.2024.01.171","article-title":"Revolutionising the sustainability of steel manufacturing using computer vision","volume":"232","author":"Giannetti","year":"2024","journal-title":"Procedia Comput. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1049\/ipr2.12647","article-title":"Review of surface defect detection of steel products based on machine vision","volume":"17","author":"Tang","year":"2023","journal-title":"IET Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016). SSD: Single Shot MultiBox Detector. Proceedings of the Computer Vision\u2014ECCV 2016, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1016\/j.procs.2022.01.135","article-title":"A Review of Yolo algorithm developments","volume":"199","author":"Jiang","year":"2022","journal-title":"Procedia Comput. Sci."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mulajkar, R., and Yede, S. (2024, January 24\u201326). YOLO version v1 to v8 comprehensive review. Proceedings of the 2024 International Conference on Inventive Computation Technologies (ICICT), Lalitpur, Nepal.","DOI":"10.1109\/ICICT60155.2024.10544452"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wen, X., Yu, Z., He, Y., Wang, Z., Chen, X., and Song, K. (2026). Deep Learning-Based Steel Surface Defect Detection: A Survey. Coatings, 16.","DOI":"10.3390\/coatings16091040"},{"key":"ref_8","unstructured":"Zhang, H., Li, M., and Zhao, K. (2024). A Feature Compensation Detection Network for Tiny Steel Surface Defects. Sensors, 24."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., and J\u00e9gou, H. (2021). Going Deeper with Image Transformers. Proceedings of the IEEE\/CVF International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"ref_10","first-page":"213","article-title":"End-to-End Object Detection with Transformers","volume":"Volume 12346","author":"Carion","year":"2020","journal-title":"Proceedings of the Computer Vision\u2014ECCV 2020"},{"key":"ref_11","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable DETR: Deformable transformers for end-to-end object detection. arXiv."},{"key":"ref_12","unstructured":"Liu, S., Li, F., Zhang, H., Yang, X., Qi, X., Su, H., Zhu, J., and Zhang, L. (2022). Dab-detr: Dynamic anchor boxes are better queries for detr. arXiv."},{"key":"ref_13","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L.M., and Shum, H.-Y. (2022). DINO: DETR with improved denoising anchor boxes for end-to-end object detection. arXiv."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024). DETRs Beat YOLOs on Real-Time Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wan, H., Wu, S., Chen, X., and Ren, P. (2025). An Improved RT-DETR Model for High-Performance Steel Surface Defect Detection. Proceedings of the 2025 IEEE 6th International Conference on Pattern Recognition, Information and Artificial Intelligence, IEEE.","DOI":"10.1109\/EIECT68017.2025.11331980"},{"key":"ref_16","first-page":"499","article-title":"Steel surface defect detection using learnable memory vision transformer","volume":"82","author":"Ayon","year":"2025","journal-title":"Comput. Mater. Contin."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, X., Chai, Y., Hu, Y., Shang, Y., Tao, Z., and Ma, Y. (2025, January 12\u201315). Steel Surface Defect Detection Algorithm Based on Improved RT-DETR. Proceedings of the 11th International Conference on Computer and Communications (ICCC), Chengdu, China.","DOI":"10.1109\/ICCC68654.2025.11437934"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, Y., Wang, Y., Zhang, Y., Tian, J., Shi, Z., Fan, J., and He, Z. (2023, January 17\u201324). SAP-DETR: Bridging the Gap Between Salient Points and Queries-Based Transformer Detector for Fast Model Convergency. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01491"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hou, X., Liu, M., Zhang, S., Wei, P., and Chen, B. (2024, January 16\u201322). Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement. Proceedings of the 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01664"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Li, T., Song, W., Chen, N., Gao, F., Wu, S., Song, C., Qi, W., Lin, Y., and Yin, R. (2025). Lightweight RT-DETR for Steel Surface Defect Detection. Proceedings of the 2025 International Symposium on Machine Learning and Social Computing, ACM.","DOI":"10.1145\/3778450.3778464"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"035235","DOI":"10.1088\/2631-8695\/adf174","article-title":"DCE-YOLOv8n: A lightweight attention-enhanced model for weld surface defect detection in low-light scenes","volume":"7","author":"Wang","year":"2025","journal-title":"Eng. Res. Express"},{"key":"ref_22","first-page":"1","article-title":"Surface defect detection of steel strips based on anchor-free network with channel attention and bidirectional feature fusion","volume":"71","author":"Yu","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ding, H., Chen, J., Ye, H., and Chen, Y. (2025). Yolo-mad: Multi-scale geometric structure feature extraction and fusion for steel surface defect detection. Appl. Sci., 15.","DOI":"10.3390\/app15147887"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, C., Chen, M., Lee, H., Li, Y., and Yu, J. (2025). A Steel Surface Defect Detection Method Based on Lightweight Convolution Optimization. arXiv.","DOI":"10.14569\/IJACSA.2025.0160619"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Guoyun, Z., Ding, P., He, J., Zhang, J., and Chongyang, Z. (2025). ELS-YOLO: Efficient lightweight YOLO for steel surface defect detection. Electronics, 14.","DOI":"10.3390\/electronics14193877"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"20154","DOI":"10.1038\/s41598-025-28022-5","article-title":"An efficient and lightweight algorithm for detecting surface defects of steel based on SCCI-YOLO","volume":"15","author":"Zhou","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e70003","DOI":"10.1002\/cpe.70003","article-title":"Surface Defect Detector Based on Deformable Convolution and Lightweight Multi-Scale Attention","volume":"37","author":"Xia","year":"2025","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"112446","DOI":"10.1016\/j.measurement.2023.112446","article-title":"FaNet: Feature-aware network for few shot classification of strip steel surface defects","volume":"208","author":"Zhao","year":"2023","journal-title":"Measurement"},{"key":"ref_29","unstructured":"Huang, W., and Wei, P. (2019). A PCB Dataset for Defects Detection and Classification. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"107984","DOI":"10.52202\/079017-3429","article-title":"YOLOv10: Real-Time End-to-End Object Detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"87151","DOI":"10.52202\/085713-2627","article-title":"YOLOv12: Attention-Centric Real-Time Object Detectors","volume":"38","author":"Tian","year":"2025","journal-title":"Adv. Neural Inf. Process. 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