{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T04:27:13Z","timestamp":1787113633053,"version":"build-2736575974"},"reference-count":48,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T00:00:00Z","timestamp":1786924800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52375557"],"award-info":[{"award-number":["52375557"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["52375557"],"award-info":[{"award-number":["52375557"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100019065","name":"Science and Technology Program of Tianjin","doi-asserted-by":"crossref","award":["23JCYBJC00060"],"award-info":[{"award-number":["23JCYBJC00060"]}],"id":[{"id":"10.13039\/501100019065","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100019065","name":"Science and Technology Program of Tianjin","doi-asserted-by":"crossref","award":["24JCYBJC00160"],"award-info":[{"award-number":["24JCYBJC00160"]}],"id":[{"id":"10.13039\/501100019065","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Foundation of Tianjin educational committee","award":["2023KJ222"],"award-info":[{"award-number":["2023KJ222"]}]},{"name":"Foundation of Tianjin educational committee","award":["2025KJ162"],"award-info":[{"award-number":["2025KJ162"]}]},{"name":"The Basic Science-research Funds of National University","award":["3122023PY06"],"award-info":[{"award-number":["3122023PY06"]}]},{"name":"The Basic Science-research Funds of National University","award":["3122024PT07"],"award-info":[{"award-number":["3122024PT07"]}]},{"name":"The Basic Science-research Funds of National University","award":["3122024052"],"award-info":[{"award-number":["3122024052"]}]},{"name":"The Basic Science-research Funds of National University","award":["3122022PY15"],"award-info":[{"award-number":["3122022PY15"]}]},{"name":"The Basic Science-research Funds of National University","award":["3122024044"],"award-info":[{"award-number":["3122024044"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Aviation ground safety and protective devices are critical for flight safety; however, their unintentional retention on aircraft after maintenance remains a persistent risk. Existing deep learning-based approaches for aviation safety have predominantly followed a reactive paradigm, detecting FOD on runways or inspecting the aircraft for inadvertently retained tools post-maintenance. In contrast, this paper advocates a proactive philosophy: using a neural network to recognize and inventory all ground safety and protective devices immediately after maintenance closure, thereby preventing retention incidents at their source. However, realizing this proactive verification is technically challenging\u2014object detection for these devices often suffers from loss of fine-grained detail due to downsampling and inherently sparse semantic information of the targets. To this end, we propose an Information Retention and Feature Screening Synergistic Network (RS-Net) grounded in information bottleneck theory. The network comprises a main branch that enhances discriminative features through attention-guided screening, and an auxiliary branch, used only during training, that preserves fine-grained spatial details via information-retentive convolutions. A Dual-State Region Refinement Module (DRM) provides configurable support for both branches, decoupling the conflicting objectives of background compression and detail preservation. Experiments on a self-constructed dataset collected from real airline maintenance operations demonstrate that RS-Net substantially outperforms the strong YOLOv9 baseline, achieving gains of 4.531% in F1-score, 2.533% in mAP0.5, and 1.429% in mAP0.5:0.95. Cross-dataset experiments further validate its strong generalization capability.<\/jats:p>","DOI":"10.3390\/jimaging12080386","type":"journal-article","created":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T11:00:05Z","timestamp":1786964405000},"page":"386","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Information Retention and Feature Screening Synergistic Network for Aviation Ground Safety and Protective Devices"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8712-0966","authenticated-orcid":false,"given":"Enming","family":"Wu","sequence":"first","affiliation":[{"name":"Engineering and Technical Training Center, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0088-0209","authenticated-orcid":false,"given":"Mingxuan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9486-8606","authenticated-orcid":false,"given":"Runxia","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2425-5641","authenticated-orcid":false,"given":"Jiusheng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7754-0286","authenticated-orcid":false,"given":"Jiaren","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Maintenance & Engineering, Qingdao Airlines Co., Ltd., Qingdao 266200, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9294-0659","authenticated-orcid":false,"given":"Fuyu","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-4559-3395","authenticated-orcid":false,"given":"Liyuan","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Aeronautical Engineering, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). Microsoft COCO: Common Objects in Context. Computer Vision\u2014ECCV 2014, Springer International Publishing.","DOI":"10.1007\/978-3-319-10590-1"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Adam, M.A.A., and Tapamo, J.R. (2025). Survey on Image-Based Vehicle Detection Methods. World Electr. Veh. J., 16.","DOI":"10.3390\/wevj16060303"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2018). Mask R-CNN. arXiv.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_6","unstructured":"Yaseen, M. (2024). What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Yeh, I.-H., and Liao, H.-Y.M. (2024). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. arXiv.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_8","unstructured":"Khanam, R., and Hussain, M. (2024). YOLOv11: An Overview of the Key Architectural Enhancements. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Tian, Y., Ye, Q., and Doermann, D. (2025). YOLOv12: Attention-Centric Real-Time Object Detectors. arXiv.","DOI":"10.52202\/085713-2627"},{"key":"ref_10","unstructured":"Lei, M., Li, S., Wu, Y., Hu, H., Zhou, Y., Zheng, X., Ding, G., Du, S., Wu, Z., and Gao, Y. (2025). YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","article-title":"SSD: Single Shot MultiBox Detector","volume":"Volume 9905","author":"Liu","year":"2016","journal-title":"Computer Vision\u2014ECCV 2016"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., Bischof, H., Brox, T., and Frahm, J.-M. (2020). End-to-End Object Detection with Transformers. Computer Vision\u2014ECCV 2020, Springer International Publishing.","DOI":"10.1007\/978-3-030-58542-6"},{"key":"ref_13","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 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 16\u201322 June 2024, IEEE.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yu, J., Jiang, Y., Wang, Z., Cao, Z., and Huang, T. (2016). UnitBox: An Advanced Object Detection Network. Proceedings of the 24th ACM International Conference on Multimedia, Amsterdam, The Netherlands, 15\u201319 October 2016, Association for Computing Machinery.","DOI":"10.1145\/2964284.2967274"},{"key":"ref_15","first-page":"12993","article-title":"Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression","volume":"34","author":"Zheng","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., and Savarese, S. (2019). Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 15\u201320 June 2019, IEEE.","DOI":"10.1109\/CVPR.2019.00075"},{"key":"ref_17","unstructured":"Ma, S., and Xu, Y. (2023). MPDIoU: A Loss for Efficient and Accurate Bounding Box Regression. arXiv."},{"key":"ref_18","unstructured":"Tong, Z., Chen, Y., Xu, Z., and Yu, R. (2023). Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Wang, D., Zhu, H., Zhao, Y., and Shi, J. (2025). A Method for Constructing a Loss Function for Multi-Scale Object Detection Networks. Sensors, 25.","DOI":"10.3390\/s25061738"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shi, S., Fang, Q., and Xu, X. (2025). Joint Optimization Loss Function for Tiny Object Detection in Remote Sensing Images. Remote Sens., 17.","DOI":"10.3390\/rs17081476"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Suvittawat, N., Kurniawan, C., Datephanyawat, J., Tay, J., Liu, Z., Soh, D.W., and Ribeiro, N.A. (2025). Advances in Aircraft Skin Defect Detection Using Computer Vision: A Survey and Comparison of YOLOv9 and RT-DETR Performance. Engineering, 12.","DOI":"10.20944\/preprints202503.0717.v1"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"115300","DOI":"10.1016\/j.measurement.2024.115300","article-title":"ASD-YOLO: An aircraft surface defects detection method using deformable convolution and attention mechanism","volume":"238","author":"Huang","year":"2024","journal-title":"Measurement"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Qu, Y., Wang, C., Xiao, Y., Yu, J., Chen, X., and Kong, Y. (2023). Optimization Algorithm for Surface Defect Detection of Aircraft Engine Components Based on YOLOv5. Appl. Sci., 13.","DOI":"10.3390\/app132011344"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3877","DOI":"10.1007\/s11760-024-03049-9","article-title":"Detection algorithm of aircraft skin defects based on improved YOLOv8n","volume":"18","author":"Wang","year":"2024","journal-title":"Signal Image Video Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"13067","DOI":"10.1038\/s41598-022-17340-7","article-title":"Deep learning-based defects detection of certain aero-engine blades and vanes with DDSC-YOLOv5s","volume":"12","author":"Li","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1109\/TAI.2025.3584026","article-title":"An Enhanced Aircraft Fuselage Defect Detection Model with Hybrid Logical-Feature Distillation","volume":"7","author":"Chen","year":"2026","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.cja.2022.05.002","article-title":"Visual inspection of aircraft skin: Automated pixel-level defect detection by instance segmentation","volume":"35","author":"Ding","year":"2022","journal-title":"Chin. J. Aeronaut."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/j.jmapro.2021.12.061","article-title":"Machine learning algorithms for defect detection in metal laser-based additive manufacturing: A review","volume":"75","author":"Fu","year":"2022","journal-title":"J. Manuf. Process."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, H.-B., Zhang, C.-Y., Cheng, D.-J., Zhou, K.-L., and Sun, Z.-Y. (2024). Detection Transformer with Multi-Scale Fusion Attention Mechanism for Aero-Engine Turbine Blade Cast Defect Detection Considering Comprehensive Features. Sensors, 24.","DOI":"10.3390\/s24051663"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"115405","DOI":"10.1088\/1361-6501\/ad6bad","article-title":"FC-YOLO: An aircraft skin defect detection algorithm based on multi-scale collaborative feature fusion","volume":"35","author":"Zhang","year":"2024","journal-title":"Meas. Sci. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Upadhyay, A., Li, J., King, S., and Addepalli, S. (2023). A Deep-Learning-Based Approach for Aircraft Engine Defect Detection. Machines, 11.","DOI":"10.3390\/machines11020192"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1213","DOI":"10.1049\/ipr2.13020","article-title":"Research on oriented surface defect detection in the aircraft skin-coating process based on an attention detector","volume":"18","author":"Zhang","year":"2024","journal-title":"IET Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"26065","DOI":"10.1109\/JSEN.2025.3567500","article-title":"MDFANet: Multidomain Feature Alignment Surface Defect Detection for Aircraft Engine Turbine Blades","volume":"25","author":"Wen","year":"2025","journal-title":"IEEE Sens. J."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.1007\/s11227-025-07818-2","article-title":"Splp-yolo: An all-weather real-time detector for airport runway foreign object debris","volume":"81","author":"Zhang","year":"2025","journal-title":"J. Supercomput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1007\/s44443-026-00534-8","article-title":"YOLO-URD: A lightweight UAV-based model for foreign object detection on airport runways","volume":"38","author":"Wang","year":"2026","journal-title":"J. King Saud. Univ. Comput. Inf. Sci."},{"key":"ref_36","first-page":"063006","article-title":"VA-YOLO: A foreign object debris detection method on the airport runway based on vision transformer and attention","volume":"33","author":"Zheng","year":"2024","journal-title":"J. Electron. Imaging"},{"key":"ref_37","first-page":"240400189-7","article-title":"Aircraft Landing Gear Safety Pin Detection Algorithm Based on Improved YOlOv5s","volume":"52","author":"Chen","year":"2025","journal-title":"Comput. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Qu, J., Liu, T., Tang, Z., Duan, Y., Yao, H., and Hu, J. (2025). Remote Sensing Small Object Detection Network Based on Multi-Scale Feature Extraction and Information Fusion. Remote Sens., 17.","DOI":"10.3390\/rs17050913"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4504","DOI":"10.1109\/TNNLS.2024.3376563","article-title":"DPNet: Dual-Path Network for Real-time Object Detection with Lightweight Attention","volume":"36","author":"Zhou","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Xu, S., Zheng, S., Xu, W., Xu, R., Wang, C., Zhang, J., Teng, X., Li, A., and Guo, L. (2024). HCF-Net: Hierarchical Context Fusion Network for Infrared Small Object Detection. arXiv.","DOI":"10.1109\/ICME57554.2024.10687431"},{"key":"ref_41","unstructured":"Kharismawati, D.E., and Kazic, T. (2025). Maize Seedling Detection Dataset (MSDD): A Curated High-Resolution RGB Dataset for Seedling Maize Detection and Benchmarking with YOLOv9, YOLO11, YOLOv12 and Faster-RCNN. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"3551","DOI":"10.1109\/TAI.2024.3372474","article-title":"A Semisupervised Aircraft Fuselage Defect Detection Network With Dynamic Attention and Class-Aware Adaptive Pseudolabel Assignment","volume":"5","author":"Chen","year":"2024","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_43","unstructured":"Vashist, A., Saadiyean, Q., Sundaram, S., and Seelamantula, C.S. (2025). FOD-S2R: A FOD Dataset for Sim2Real Transfer Learning based Object Detection. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5325","DOI":"10.1109\/TPAMI.2024.3366349","article-title":"A Survey on Information Bottleneck","volume":"46","author":"Hu","year":"2024","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","unstructured":"Tishby, N., Pereira, F.C., and Bialek, W. (2000). The information bottleneck method. arXiv."},{"key":"ref_46","unstructured":"Gomez, A.N., Ren, M., Urtasun, R., and Grosse, R.B. (2017). The Reversible Residual Network: Backpropagation Without Storing Activations. arXiv."},{"key":"ref_47","unstructured":"Dinh, L., Krueger, D., and Bengio, Y. (2015). NICE: Non-linear Independent Components Estimation. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"379\u2013423, 623\u2013656","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A Mathematical Theory of Communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell Syst. Tech. J."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/8\/386\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T04:13:20Z","timestamp":1787112800000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/8\/386"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,17]]},"references-count":48,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["jimaging12080386"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12080386","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,17]]}}}