{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T18:02:05Z","timestamp":1790359325041,"version":"4.1.0"},"reference-count":72,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T00:00:00Z","timestamp":1790294400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["No. 2024JC-YBMS-564"],"award-info":[{"award-number":["No. 2024JC-YBMS-564"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Steel surface defect detection is a critical component of intelligent manufacturing and industrial visual quality control. However, due to large variations in defect scale, elongated morphologies, strong directional continuity, and high inter-class visual similarity, existing detection methods struggle to achieve a desirable balance among global structural modeling, semantically adaptive representation, and computational efficiency. To address these challenges, this paper proposes an efficient steel surface defect detection framework, termed EMamba. By integrating state space modeling, conditional expert computation, and a hybrid multi-branch architectural design, the proposed framework systematically enhances detection performance in complex industrial scenarios. Specifically, we introduce a two-dimensional state space modeling mechanism and propose a Convolutional State Space Block (CSSBlock), which models long-range spatial dependencies and directional continuity with linear computational complexity. At the high-level semantic stage of the backbone network, an Efficient Sparse Mixture-of-Experts module (ES-MoE) is incorporated. Through a conditional Top-k routing mechanism, ES-MoE enhances the diversity and discriminability of feature representations. Furthermore, we propose a Mixed Star Network (MSN), which selectively configures high-order feature modeling modules in a hierarchy-aware manner within a multi-branch structure, achieving a fine-grained trade-off between representational capacity and computational efficiency. Building upon these components, a decoupled detection head is adopted to construct the unified EMamba detection framework. Extensive experiments on multiple steel surface defect datasets demonstrate that EMamba achieves competitive detection accuracy, structural defect perception capability, and inference efficiency compared with representative existing methods. These results indicate the effectiveness and practicality of the proposed hybrid modeling paradigm for steel surface defect detection.<\/jats:p>","DOI":"10.1371\/journal.pone.0358866","type":"journal-article","created":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T17:40:07Z","timestamp":1790358007000},"page":"e0358866","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["EMamba: A MoE-enhanced state space network for robust steel surface defect detection"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5969-1780","authenticated-orcid":true,"given":"Haiyan","family":"Yang","sequence":"first","affiliation":[{"name":"Technological Institute of Materials & Energy Science, Xijing University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Wang","sequence":"additional","affiliation":[{"name":"Technological Institute of Materials & Energy Science, Xijing University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangyang","family":"Liu","sequence":"additional","affiliation":[{"name":"Technological Institute of Materials & Energy Science, Xijing University","place":["Xi\u2019an, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,9,25]]},"reference":[{"issue":"2","key":"pone.0358866.ref001","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1049\/ipr2.12647","article-title":"Review of surface defect detection of steel products based on machine vision","volume":"17","author":"B Tang","year":"2022","journal-title":"IET Image Processing"},{"issue":"6","key":"pone.0358866.ref002","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1016\/S0031-3203(00)00071-6","article-title":"Automatic surface inspection using wavelet reconstruction","volume":"34","author":"D-M Tsai","year":"2001","journal-title":"Pattern Recognition"},{"key":"pone.0358866.ref003","doi-asserted-by":"crossref","first-page":"122361","DOI":"10.1016\/j.eswa.2023.122361","article-title":"Industrial surface defect detection and localization using multi-scale information focusing and enhancement GANomaly","volume":"238","author":"J Peng","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"pone.0358866.ref004","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2023.3277989","article-title":"Steel surface defect detection via deformable convolution and background suppression","volume":"72","author":"C Song","year":"2023","journal-title":"IEEE Trans Instrum Meas"},{"issue":"1","key":"pone.0358866.ref005","article-title":"CRGF-YOLO: An optimized multi-scale feature fusion model based on YOLOv5 for detection of steel surface defects","volume":"17","author":"T Yu","year":"2024","journal-title":"Int J Comput Intell Syst"},{"key":"pone.0358866.ref006","doi-asserted-by":"crossref","first-page":"123199","DOI":"10.1016\/j.eswa.2024.123199","article-title":"A real-time anchor-free defect detector with global and local feature enhancement for surface defect detection","volume":"246","author":"Q Liu","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"pone.0358866.ref007","doi-asserted-by":"crossref","first-page":"124661","DOI":"10.1016\/j.eswa.2024.124661","article-title":"EL-Net: An efficient and lightweight optimized network for object detection in remote sensing images","volume":"255","author":"C Dong","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"pone.0358866.ref008","first-page":"1","article-title":"Multiscale attention networks for pavement defect detection","volume":"72","author":"J Chen","year":"2023","journal-title":"IEEE Trans Instrum Meas"},{"issue":"1","key":"pone.0358866.ref009","doi-asserted-by":"crossref","first-page":"7671","DOI":"10.1038\/s41598-024-57990-3","article-title":"Surface defect detection of hot rolled steel based on multi-scale feature fusion and attention mechanism residual block","volume":"14","author":"H Zhang","year":"2024","journal-title":"Sci Rep"},{"issue":"1","key":"pone.0358866.ref010","doi-asserted-by":"crossref","first-page":"25098","DOI":"10.1038\/s41598-025-10647-1","article-title":"Steel surface defect detection method based on improved YOLOv9","volume":"15","author":"C Chen","year":"2025","journal-title":"Sci Rep"},{"issue":"5","key":"pone.0358866.ref011","doi-asserted-by":"crossref","first-page":"6705","DOI":"10.1007\/s40747-024-01512-1","article-title":"DFFNet: A lightweight approach for efficient feature-optimized fusion in steel strip surface defect detection","volume":"10","author":"X Hu","year":"2024","journal-title":"Complex Intell Syst"},{"issue":"1","key":"pone.0358866.ref012","article-title":"EC-PFN: A multiscale woven fusion network for industrial product surface defect detection","volume":"11","author":"S Liu","year":"2024","journal-title":"Complex Intell Syst"},{"key":"pone.0358866.ref013","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"S Ren","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"pone.0358866.ref014","doi-asserted-by":"crossref","unstructured":"Redmon J, Divvala S, Girshick R. You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016. 779\u201388.","DOI":"10.1109\/CVPR.2016.91"},{"key":"pone.0358866.ref015","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A. YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017. 7263\u201371.","DOI":"10.1109\/CVPR.2017.690"},{"key":"pone.0358866.ref016","article-title":"Yolov4: Optimal speed and accuracy of object detection","author":"A Bochkovskiy","year":"2020","journal-title":"arXiv preprint"},{"key":"pone.0358866.ref017","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, et al. Ssd: Single shot multibox detector. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I, 2016. 21\u201337.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"pone.0358866.ref018","doi-asserted-by":"crossref","unstructured":"Duan K, Bai S, Xie L, Qi H, Huang Q, Tian Q. CenterNet: Keypoint Triplets for Object Detection. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), 2019. 6568\u201377. https:\/\/doi.org\/10.1109\/iccv.2019.00667","DOI":"10.1109\/ICCV.2019.00667"},{"key":"pone.0358866.ref019","doi-asserted-by":"crossref","unstructured":"Tian Z, Shen C, Chen H, He T. FCOS: Fully Convolutional One-Stage Object Detection. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), 2019. 9626\u201335. https:\/\/doi.org\/10.1109\/iccv.2019.00972","DOI":"10.1109\/ICCV.2019.00972"},{"key":"pone.0358866.ref020","article-title":"Yolox: Exceeding Yolo Series in 2021","author":"Z Ge","year":"2021","journal-title":"arXiv preprint"},{"key":"pone.0358866.ref021","article-title":"Rtmdet: An empirical study of designing real-time object detectors","author":"C Lyu","year":"2022","journal-title":"arXiv preprint"},{"key":"pone.0358866.ref022","article-title":"YOLOv6: A single-stage object detection framework for industrial applications","author":"C Li","year":"2022","journal-title":"arXiv preprint"},{"key":"pone.0358866.ref023","doi-asserted-by":"crossref","unstructured":"Wang CY, Bochkovskiy A, Liao HYM. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 2023. 7464\u201375.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"pone.0358866.ref024","doi-asserted-by":"crossref","unstructured":"Wang CY, Yeh IH, Mark Liao HY. Yolov9: Learning what you want to learn using programmable gradient information. In: European conference on computer vision, 2024. 1\u201321.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"pone.0358866.ref025","doi-asserted-by":"crossref","unstructured":"Wang A, Chen H, Liu L, Chen K, Lin Z, Han J, et al. YOLOv10: Real-Time End-to-End Object Detection. In: Advances in Neural Information Processing Systems 37, 2024. 107984\u20138011. https:\/\/doi.org\/10.52202\/079017-3429","DOI":"10.52202\/079017-3429"},{"key":"pone.0358866.ref026","doi-asserted-by":"crossref","unstructured":"Tian Y, Ye Q, Doermann D. YOLOv12: Attention-Centric Real-Time Object Detectors. In: Advances in Neural Information Processing Systems 38, 2025. 87151\u201375. https:\/\/doi.org\/10.52202\/085713-2627","DOI":"10.52202\/085713-2627"},{"key":"pone.0358866.ref027","article-title":"YOLOv13: Real-time object detection with hypergraph-enhanced adaptive visual perception","author":"M Lei","year":"2025","journal-title":"arXiv preprint"},{"key":"pone.0358866.ref028","article-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"A Dosovitskiy","year":"2020","journal-title":"arXiv preprint"},{"key":"pone.0358866.ref029","first-page":"213","volume-title":"End-to-end object detection with transformers. European conference on computer vision","author":"N Carion","year":"2020"},{"key":"pone.0358866.ref030","article-title":"Deformable detr: Deformable transformers for end-to-end object detection","author":"X Zhu","year":"2020","journal-title":"arXiv preprint"},{"key":"pone.0358866.ref031","article-title":"Dino: Detr with improved denoising anchor boxes for end-to-end object detection","author":"H Zhang","year":"2022","journal-title":"arXiv preprint"},{"key":"pone.0358866.ref032","doi-asserted-by":"crossref","unstructured":"Zhao Y, Lv W, Xu S, et al. Detrs beat yolos on real-time object detection. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2024: 16965\u201374.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"pone.0358866.ref033","doi-asserted-by":"crossref","unstructured":"Huang S, Lu Z, Cun X, et al. Deim: Detr with improved matching for fast convergence. In: Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 15162\u201371.","DOI":"10.1109\/CVPR52734.2025.01412"},{"issue":"1","key":"pone.0358866.ref034","doi-asserted-by":"crossref","first-page":"19649","DOI":"10.1038\/s41598-025-04811-w","article-title":"RSTD-YOLOv7: A steel surface defect detection based on improved YOLOv7","volume":"15","author":"H Song","year":"2025","journal-title":"Sci Rep"},{"key":"pone.0358866.ref035","first-page":"1","article-title":"Efficient fused-attention model for steel surface defect detection","author":"C-C Yeung","year":"2022","journal-title":"IEEE Trans Instrum Meas"},{"issue":"1","key":"pone.0358866.ref036","doi-asserted-by":"crossref","first-page":"8966","DOI":"10.1038\/s41598-025-93469-5","article-title":"A lightweight algorithm for steel surface defect detection using improved YOLOv8","volume":"15","author":"S Ma","year":"2025","journal-title":"Sci Rep"},{"issue":"1","key":"pone.0358866.ref037","doi-asserted-by":"crossref","first-page":"13267","DOI":"10.1038\/s41598-024-64080-x","article-title":"Lightweight strip steel defect detection algorithm based on improved YOLOv7","volume":"14","author":"J Lu","year":"2024","journal-title":"Sci Rep"},{"key":"pone.0358866.ref038","doi-asserted-by":"crossref","first-page":"91499","DOI":"10.1109\/ACCESS.2024.3422244","article-title":"Surface defect detection of steel plate based on SKS-YOLO","volume":"12","author":"S Zhou","year":"2024","journal-title":"IEEE Access"},{"issue":"3","key":"pone.0358866.ref039","doi-asserted-by":"crossref","first-page":"3943","DOI":"10.1007\/s40747-024-01362-x","article-title":"TD-Net:tiny defect detection network for industrial products","volume":"10","author":"R Shao","year":"2024","journal-title":"Complex Intell Syst"},{"issue":"4","key":"pone.0358866.ref040","doi-asserted-by":"crossref","first-page":"6684","DOI":"10.1109\/TII.2023.3347747","article-title":"PMSA-DyTr: Prior-modulated and semantic-aligned dynamic transformer for strip steel defect detection","volume":"20","author":"J Su","year":"2024","journal-title":"IEEE Trans Ind Inf"},{"key":"pone.0358866.ref041","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2023.3330222","article-title":"PENet-KD: Progressive Enhancement Network via Knowledge Distillation for Rail Surface Defect Detection","volume":"72","author":"B Wang","year":"2023","journal-title":"IEEE Trans Instrum Meas"},{"key":"pone.0358866.ref042","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2023.3277989","article-title":"Steel surface defect detection via deformable convolution and background suppression","volume":"72","author":"C Song","year":"2023","journal-title":"IEEE Trans Instrum Meas"},{"key":"pone.0358866.ref043","unstructured":"Gu A, Dao T. Mamba: Linear-time sequence modeling with selective state spaces. In: First conference on language modeling. 2024."},{"key":"pone.0358866.ref044","unstructured":"Zhu L, Liao B, Zhang Q, Wang X, Liu W, Wang X. Vision Mamba: Efficient visual representation learning with bidirectional state space model. In: Proc. 41st Int. Conf. Mach. Learn. (ICML), 2024. 62429\u201342."},{"key":"pone.0358866.ref045","doi-asserted-by":"crossref","unstructured":"Liu Y, Tian Y, Zhao Y, Yu H, Xie L, Wang Y, et al. VMamba: Visual State Space Model. In: Advances in Neural Information Processing Systems 37, 2024. 103031\u201363. https:\/\/doi.org\/10.52202\/079017-3273","DOI":"10.52202\/079017-3273"},{"key":"pone.0358866.ref046","doi-asserted-by":"crossref","unstructured":"Lee S, Choi J, Kim HJ. Efficientvim: Efficient vision mamba with hidden state mixer based state space duality. In: Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 14923\u201333.","DOI":"10.1109\/CVPR52734.2025.01390"},{"key":"pone.0358866.ref047","doi-asserted-by":"crossref","unstructured":"Lin Y-C, Xu Y-S, Chen H-W, Kuo H-K, Lee C-Y. EAMamba: Efficient All-Around Vision State Space Model for Image Restoration. In: 2025 IEEE\/CVF International Conference on Computer Vision (ICCV), 2025. 11708\u201319. https:\/\/doi.org\/10.1109\/iccv51701.2025.01089","DOI":"10.1109\/ICCV51701.2025.01089"},{"key":"pone.0358866.ref048","doi-asserted-by":"crossref","unstructured":"Hatamizadeh A, Kautz J. Mambavision: A hybrid mamba-transformer vision backbone. In: Proceedings of the Computer Vision and Pattern Recognition Conference, 2025. 25261\u201370.","DOI":"10.1109\/CVPR52734.2025.02352"},{"issue":"8","key":"pone.0358866.ref049","doi-asserted-by":"crossref","first-page":"8205","DOI":"10.1609\/aaai.v39i8.32885","article-title":"Mamba YOLO: A simple baseline for object detection with state space model","volume":"39","author":"Z Wang","year":"2025","journal-title":"AAAI"},{"key":"pone.0358866.ref050","article-title":"Transmamba: Fast universal architecture adaption from transformers to mamba","author":"X Chen","year":"2025","journal-title":"arXiv preprint"},{"key":"pone.0358866.ref051","doi-asserted-by":"crossref","unstructured":"Huang T, Pei X, You S. Localmamba: Visual state space model with windowed selective scan. In: European conference on computer vision, 2024. 12\u201322.","DOI":"10.1007\/978-3-031-91979-4_2"},{"key":"pone.0358866.ref052","unstructured":"Yang C, Chen Z, Espinosa M, Ericsson L, Wang Z, Liu J, et al. PlainMamba: Improving non-hierarchical Mamba in visual recognition. In: Proc. 35th Brit. Mach. Vis. Conf. (BMVC), Glasgow, U.K., 2024."},{"key":"pone.0358866.ref053","doi-asserted-by":"crossref","unstructured":"Li S, Singh H, Grover A. Mamba-nd: Selective state space modeling for multi-dimensional data. In: European Conference on Computer Vision, 2024. 75\u201392.","DOI":"10.1007\/978-3-031-73414-4_5"},{"key":"pone.0358866.ref054","unstructured":"Shazeer N, Mirhoseini A, Maziarz K, Davis A, Le QV, Hinton G, et al. Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer. In: Proceedings of the International Conference on Learning Representations (ICLR), 2017."},{"key":"pone.0358866.ref055","article-title":"Mofe-time: Mixture of frequency domain experts for time-series forecasting models","author":"Y Liu","year":"2025","journal-title":"arXiv preprint"},{"key":"pone.0358866.ref056","article-title":"Moh: Multi-head attention as mixture-of-head attention","author":"P Jin","year":"2024","journal-title":"arXiv preprint"},{"key":"pone.0358866.ref057","doi-asserted-by":"crossref","unstructured":"Wang X, Jin Y, Wu W, et al. Object detection using event camera: A moe heat conduction based detector and a new benchmark dataset. Proceedings of the Computer Vision and Pattern Recognition Conference. 2025: 29321\u201330.","DOI":"10.1109\/CVPR52734.2025.02730"},{"key":"pone.0358866.ref058","doi-asserted-by":"crossref","unstructured":"Song Z, Xia C, Wang C. UniMM-V2X: MoE-Enhanced Multi-Level Fusion for End-to-End Cooperative Autonomous Driving. In: Proceedings of the AAAI Conference on Artificial Intelligence, 2026. 9135\u201343.","DOI":"10.1609\/aaai.v40i11.37870"},{"key":"pone.0358866.ref059","unstructured":"Lin X, Peng J, Gan Z. Yolo-master: Moe-accelerated with specialized transformers for enhanced real-time detection. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 2026. 18440\u20139."},{"issue":"6","key":"pone.0358866.ref060","article-title":"MLK-TR: A Multi-branch Large Kernel TRansformer for UAV-based images","volume":"11","author":"X Li","year":"2025","journal-title":"Complex Intell Syst"},{"key":"pone.0358866.ref061","doi-asserted-by":"crossref","first-page":"122669","DOI":"10.1016\/j.eswa.2023.122669","article-title":"DsP-YOLO: An anchor-free network with DsPAN for small object detection of multiscale defects","volume":"241","author":"Y Zhang","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"pone.0358866.ref062","doi-asserted-by":"crossref","first-page":"110176","DOI":"10.1016\/j.knosys.2022.110176","article-title":"Progressive refined redistribution pyramid network for defect detection in complex scenarios","volume":"260","author":"X Yu","year":"2023","journal-title":"Knowledge-Based Systems"},{"key":"pone.0358866.ref063","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":"J Yu","year":"2022","journal-title":"IEEE Trans Instrum Meas"},{"key":"pone.0358866.ref064","doi-asserted-by":"crossref","first-page":"130339","DOI":"10.1109\/ACCESS.2022.3227205","article-title":"LFF-YOLO: A YOLO algorithm with lightweight feature fusion network for multi-scale defect detection","volume":"10","author":"X Qian","year":"2022","journal-title":"IEEE Access"},{"key":"pone.0358866.ref065","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":"R Tian","year":"2022","journal-title":"Measurement"},{"issue":"8","key":"pone.0358866.ref066","doi-asserted-by":"crossref","first-page":"8205","DOI":"10.1609\/aaai.v39i8.32885","article-title":"Mamba YOLO: A simple baseline for object detection with state space model","volume":"39","author":"Z Wang","year":"2025","journal-title":"AAAI"},{"key":"pone.0358866.ref067","doi-asserted-by":"crossref","unstructured":"Qin D, Leichner C, Delakis M. MobileNetV4: Universal models for the mobile ecosystem. In: European conference on computer vision, 2024. 78\u201396.","DOI":"10.1007\/978-3-031-73661-2_5"},{"key":"pone.0358866.ref068","doi-asserted-by":"crossref","unstructured":"Chen J, Kao S, He H, et al. Run, don\u2019t walk: chasing higher FLOPS for faster neural networks. In: Proc. IEEE\/CVF Conf. Computer. Vis. Pattern Recognition, 2023. 12021\u201331.","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"pone.0358866.ref069","doi-asserted-by":"crossref","unstructured":"Ma X, Dai X, Bai Y, Wang Y, Fu Y. Rewrite the Stars. In: 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024. 5694\u2013703. https:\/\/doi.org\/10.1109\/cvpr52733.2024.00544","DOI":"10.1109\/CVPR52733.2024.00544"},{"key":"pone.0358866.ref070","doi-asserted-by":"crossref","unstructured":"Li Y, Hou Q, Zheng Z, Cheng M-M, Yang J, Li X. Large Selective Kernel Network for Remote Sensing Object Detection. In: 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), 2023. 16748\u201359. https:\/\/doi.org\/10.1109\/iccv51070.2023.01540","DOI":"10.1109\/ICCV51070.2023.01540"},{"key":"pone.0358866.ref071","article-title":"Reversible column networks","author":"Y Cai","year":"2022","journal-title":"arXiv preprint"},{"key":"pone.0358866.ref072","article-title":"Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks","author":"RL Draelos","year":"2020","journal-title":"arXiv preprint"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0358866","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T17:40:25Z","timestamp":1790358025000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0358866"}},"subtitle":[],"editor":[{"given":"Muhammad Waqar","family":"Akram","sequence":"first","affiliation":[{"name":"Hohai University","place":["CHINA"]}],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,9,25]]},"references-count":72,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9,25]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0358866","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,25]]}}}