{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T04:37:12Z","timestamp":1776400632316,"version":"3.51.2"},"reference-count":51,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T00:00:00Z","timestamp":1718755200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Foundation of China","award":["62261053"],"award-info":[{"award-number":["62261053"]}]},{"name":"National Science Foundation of China","award":["BNR2019TD01022"],"award-info":[{"award-number":["BNR2019TD01022"]}]},{"name":"National Science Foundation of China","award":["2023TSYCTD0012"],"award-info":[{"award-number":["2023TSYCTD0012"]}]},{"name":"Cross-Media Intelligent Technology Project of Beijing National Research Center for Information Science and Technology (BNRist)","award":["62261053"],"award-info":[{"award-number":["62261053"]}]},{"name":"Cross-Media Intelligent Technology Project of Beijing National Research Center for Information Science and Technology (BNRist)","award":["BNR2019TD01022"],"award-info":[{"award-number":["BNR2019TD01022"]}]},{"name":"Cross-Media Intelligent Technology Project of Beijing National Research Center for Information Science and Technology (BNRist)","award":["2023TSYCTD0012"],"award-info":[{"award-number":["2023TSYCTD0012"]}]},{"name":"Tianshan Talent Training Project-Xinjiang Science and Technology Innovation Team Program","award":["62261053"],"award-info":[{"award-number":["62261053"]}]},{"name":"Tianshan Talent Training Project-Xinjiang Science and Technology Innovation Team Program","award":["BNR2019TD01022"],"award-info":[{"award-number":["BNR2019TD01022"]}]},{"name":"Tianshan Talent Training Project-Xinjiang Science and Technology Innovation Team Program","award":["2023TSYCTD0012"],"award-info":[{"award-number":["2023TSYCTD0012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The automatic monitoring and detection of maritime targets hold paramount significance in safeguarding national sovereignty, ensuring maritime rights, and advancing national development. Among the principal means of maritime surveillance, infrared (IR) small ship detection technology stands out. However, due to their minimal pixel occupancy and lack of discernible color and texture information, IR small ships have persistently posed a formidable challenge in the realm of target detection. Additionally, the intricate maritime backgrounds often exacerbate the issue by inducing high false alarm rates. In an effort to surmount these challenges, this paper proposes a flexible convolutional network (FCNet), integrating dilated convolutions and deformable convolutions to achieve flexible variations in convolutional receptive fields. Firstly, a feature enhancement module (FEM) is devised to enhance input features by fusing standard convolutions with dilated convolutions, thereby obtaining precise feature representations. Subsequently, a context fusion module (CFM) is designed to integrate contextual information during the downsampling process, mitigating information loss. Furthermore, a semantic fusion module (SFM) is crafted to fuse shallow features with deep semantic information during the upsampling process. Additionally, squeeze-and-excitation (SE) blocks are incorporated during upsampling to bolster channel information. Experimental evaluations conducted on two datasets demonstrate that FCNet outperforms other algorithms in the detection of IR small ships on maritime surfaces. Moreover, to propel research in deep learning-based IR small ship detection on maritime surfaces, we introduce the IR small ship dataset (Maritime-SIRST).<\/jats:p>","DOI":"10.3390\/rs16122218","type":"journal-article","created":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T04:21:28Z","timestamp":1718770888000},"page":"2218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["FCNet: Flexible Convolution Network for Infrared Small Ship Detection"],"prefix":"10.3390","volume":"16","author":[{"given":"Feng","family":"Guo","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1785-4024","authenticated-orcid":false,"given":"Hongbing","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China"},{"name":"Department of Electronic Engineering, Tsinghua University, Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7354-7494","authenticated-orcid":false,"given":"Liangliang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Ming","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China"}]},{"given":"Zhenhong","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xinjiang University, Urumqi 830046, China"},{"name":"Key Laboratory of Signal Detection and Processing, Xinjiang University, Urumqi 830046, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4324","DOI":"10.1109\/TGRS.2020.3008993","article-title":"Ship detection in spaceborne infrared image based on lightweight CNN and multisource feature cascade decision","volume":"59","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Li, L., Jiang, L., Zhang, J., Wang, S., and Chen, F. (2022). A complete YOLO-based ship detection method for thermal infrared remote sensing images under complex backgrounds. Remote Sens., 14.","DOI":"10.3390\/rs14071534"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, L., Ma, H., and Jia, Z. (2022). Multiscale geometric analysis fusion-based unsupervised change detection in remote sensing images via FLICM model. Entropy, 24.","DOI":"10.3390\/e24020291"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5003614","DOI":"10.1109\/TGRS.2022.3218563","article-title":"SRCANet: Stacked residual coordinate attention network for infrared ship detection","volume":"60","author":"Wu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","first-page":"5610013","article-title":"Progressive task-based universal network for raw infrared remote sensing imagery ship detection","volume":"61","author":"Li","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"5612912","DOI":"10.1109\/TGRS.2023.3286836","article-title":"Infrared small target tracking algorithm via segmentation network and multi-strategy fusion","volume":"61","author":"Kou","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"109788","DOI":"10.1016\/j.patcog.2023.109788","article-title":"Infrared small target segmentation networks: A survey","volume":"143","author":"Kou","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_8","first-page":"3506305","article-title":"Fast ship detection with spatial-frequency analysis and ANOVA-based feature fusion","volume":"19","author":"Zhang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/MGRS.2022.3145502","article-title":"Single-frame infrared small-target detection: A survey","volume":"10","author":"Zhao","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5000917","DOI":"10.1109\/TGRS.2023.3243062","article-title":"One-stage cascade refinement networks for infrared small target detection","volume":"61","author":"Dai","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, L., Lv, M., Jia, Z., and Ma, H. (2023). Sparse representation-based multi-focus image fusion method via local energy in shearlet domain. Sensors, 23.","DOI":"10.3390\/s23062888"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103657","DOI":"10.1016\/j.infrared.2021.103657","article-title":"Infrared maritime dim small target detection based on spatiotemporal cues and directional morphological filtering","volume":"115","author":"Li","year":"2021","journal-title":"Infrared Phys. Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2369","DOI":"10.1109\/JSTARS.2021.3049847","article-title":"Infrared small maritime target detection based on integrated target saliency measure","volume":"14","author":"Yang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1442","DOI":"10.1109\/LGRS.2019.2898893","article-title":"A local contrast method combined with adaptive background estimation for infrared small target detection","volume":"16","author":"Han","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","first-page":"1309","article-title":"Infrared dim moving target tracking via improved context learning","volume":"Volume 10255","author":"Qian","year":"2017","journal-title":"Selected Papers of the Chinese Society for Optical Engineering Conferences Held October and November 2016"},{"key":"ref_16","first-page":"7000605","article-title":"Small infrared target detection based on fast adaptive masking and scaling with iterative segmentation","volume":"19","author":"Chen","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Guan, X., Zhang, L., Huang, S., and Peng, Z. (2020). Infrared small target detection via non-convex tensor rank surrogate joint local contrast energy. Remote Sens., 12.","DOI":"10.3390\/rs12091520"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6144","DOI":"10.1109\/JSTARS.2022.3193884","article-title":"Infrared small target detection based on the improved density peak global search and human visual local contrast mechanism","volume":"15","author":"Kou","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","first-page":"7506205","article-title":"ISTDU-Net: Infrared small-target detection U-Net","volume":"19","author":"Hou","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","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":"2019","journal-title":"IEEE Ttrans. Pattern Anal. Mach. Intell."},{"key":"ref_21","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."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Law, H., and Deng, J. (2018, January 8\u201314). Cornernet: Detecting objects as paired keypoints. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_25","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., and Polosukhin, I. (2017). Attention is all you need. Adv. Neural Inf. Process. Syst., 30."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201323). Non-local neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_27","unstructured":"Wang, H., Zhou, L., and Wang, L. (November, January 27). Miss detection vs. false alarm: Adversarial learning for small object segmentation in infrared images. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Dai, Y., Wu, Y., Zhou, F., and Barnard, K. (2021, January 5\u20139). Asymmetric contextual modulation for infrared small target detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Virtual.","DOI":"10.1109\/WACV48630.2021.00099"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1109\/TIP.2022.3199107","article-title":"Dense nested attention network for infrared small target detection","volume":"32","author":"Li","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pan, P., Wang, H., Wang, C., and Nie, C. (2023, January 10\u201314). ABC: Attention with bilinear correlation for infrared small target detection. Proceedings of the 2023 IEEE International Conference on Multimedia and Expo (ICME), Brisbane, Australia.","DOI":"10.1109\/ICME55011.2023.00406"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, W., Dai, J., Chen, Z., Huang, Z., Li, Z., Zhu, X., Hu, X., Lu, T., Lu, L., and Li, H. (2023, January 18\u201322). Internimage: Exploring large-scale vision foundation models with deformable convolutions. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01385"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xiong, Y., Li, Z., Chen, Y., Wang, F., Zhu, X., Luo, J., Wang, W., Lu, T., Li, H., and Qiao, Y. (2024). Efficient deformable convnets: Rethinking dynamic and sparse operator for vision applications. arXiv.","DOI":"10.1109\/CVPR52733.2024.00540"},{"key":"ref_34","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, M., Zhang, R., Yang, Y., Bai, H., Zhang, J., and Guo, J. (2022, January 18\u201324). ISNet: Shape matters for infrared small target detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00095"},{"key":"ref_36","first-page":"5000219","article-title":"KCPNet: Knowledge-driven context perception networks for ship detection in infrared imagery","volume":"61","author":"Han","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","first-page":"5601015","article-title":"MTU-Net: Multilevel transunet for space-based infrared tiny ship detection","volume":"61","author":"Wu","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dai, Y., Gieseke, F., Oehmcke, S., Wu, Y., and Barnard, K. (2021, January 5\u20139). Attentional feature fusion. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00360"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"9813","DOI":"10.1109\/TGRS.2020.3044958","article-title":"Attentional local contrast networks for infrared small target detection","volume":"59","author":"Dai","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.1016\/j.patcog.2009.12.023","article-title":"Analysis of new top-hat transformation and the application for infrared dim small target detection","volume":"43","author":"Bai","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.patcog.2016.04.002","article-title":"Multiscale patch-based contrast measure for small infrared target detection","volume":"58","author":"Wei","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"869","DOI":"10.37188\/OPE.20223007.0869","article-title":"Infrared small target detection using tri-layer template local difference measure","volume":"30","author":"Mu","year":"2022","journal-title":"Opt. Precis. Eng."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"4250","DOI":"10.1109\/TAES.2023.3238703","article-title":"Attention-guided pyramid context networks for detecting infrared small target under complex background","volume":"59","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"5621313","DOI":"10.1109\/TGRS.2023.3314586","article-title":"LW-IRSTNet: Lightweight infrared small target segmentation network and application deployment","volume":"61","author":"Kou","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Guo, F., Ma, H., Li, L., Lv, M., and Jia, Z. (2024). Multi-attention pyramid context network for infrared small ship detection. J. Mar. Sci. Eng., 12.","DOI":"10.3390\/jmse12020345"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, L., Ma, H., and Jia, Z. (2021). Change detection from SAR images based on convolutional neural networks guided by saliency enhancement. Remote Sens., 13.","DOI":"10.3390\/rs13183697"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1109\/JBHI.2024.3350245","article-title":"FD-Net: Feature distillation network for oral squamous cell carcinoma lymph node segmentation in hyperspectral imagery","volume":"28","author":"Zhang","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"120615","DOI":"10.1016\/j.eswa.2023.120615","article-title":"Hyperspectral pathology image classification using dimension-driven multi-path attention residual network","volume":"230","author":"Zhang","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1077","DOI":"10.1007\/s12524-023-01674-4","article-title":"Gamma correction-based automatic unsupervised change detection in SAR images via FLICM model","volume":"51","author":"Li","year":"2023","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Li, L., Ma, H., Zhang, X., Zhao, X., Lv, M., and Jia, Z. (2024). Synthetic aperture radar image change detection based on principal component analysis and two-level clustering. Remote Sens., 16.","DOI":"10.3390\/rs16111861"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/12\/2218\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:00:54Z","timestamp":1760108454000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/12\/2218"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,19]]},"references-count":51,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16122218"],"URL":"https:\/\/doi.org\/10.3390\/rs16122218","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,19]]}}}