{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T12:34:25Z","timestamp":1777552465484,"version":"3.51.4"},"reference-count":39,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T00:00:00Z","timestamp":1750550400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["11702289"],"award-info":[{"award-number":["11702289"]}]},{"name":"National Natural Science Foundation of China","award":["2020XXX013"],"award-info":[{"award-number":["2020XXX013"]}]},{"name":"Key core technology and generic technology research and development project of Shanxi Province","award":["11702289"],"award-info":[{"award-number":["11702289"]}]},{"name":"Key core technology and generic technology research and development project of Shanxi Province","award":["2020XXX013"],"award-info":[{"award-number":["2020XXX013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Maritime object detection is essential for resource monitoring, maritime defense, and public safety, yet detecting diverse targets beyond ships remains challenging. This paper presents YOLO-SEA, an efficient detection framework based on the enhanced YOLOv8 architecture. The model incorporates the SESA (SimAM-Enhanced SENetV2 Attention) module, which integrates the channel-adaptive weight adjustment of SENetV2 with the parameter-free spatial-channel modeling of SimAM to enhance feature representation. An improved BiFPN (Bidirectional Feature Pyramid Network) structure enhances multi-scale fusion, particularly for small object detection. In the post-processing stage, Soft-NMS (Soft Non-Maximum Suppression) replaces traditional NMS to reduce false suppression in dense scenes. YOLO-SEA detects eight maritime object types. Experiments show it achieves a 5.8% improvement in mAP@0.5 and 7.2% improvement in mAP@0.5:0.95 over the baseline, demonstrating enhanced accuracy and robustness in complex marine environments.<\/jats:p>","DOI":"10.3390\/e27070667","type":"journal-article","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T09:08:44Z","timestamp":1750669724000},"page":"667","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["YOLO-SEA: An Enhanced Detection Framework for Multi-Scale Maritime Targets in Complex Sea States and Adverse Weather"],"prefix":"10.3390","volume":"27","author":[{"given":"Hongmei","family":"Deng","sequence":"first","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuaiqun","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3326-7030","authenticated-orcid":false,"given":"Xinyao","family":"Wang","sequence":"additional","affiliation":[{"name":"Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Zheng","sequence":"additional","affiliation":[{"name":"Innovation Academy for Microsatellites of Chinese Academy of Sciences, Shanghai 201210, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8218-7195","authenticated-orcid":false,"given":"Yanli","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,22]]},"reference":[{"key":"ref_1","first-page":"13","article-title":"A Target Identification Technique for Unmanned Surface Vessel Based on Deep Learning","volume":"43","author":"Wang","year":"2022","journal-title":"Acta Armamentarii"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yang, D., Solihin, M.I., Ardiyanto, I., Zhao, Y., Li, W., Cai, B., and Chen, C. (2024). A streamlined approach for intelligent ship object detection using EL-YOLO algorithm. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-64225-y"},{"key":"ref_3","first-page":"1657","article-title":"MCMOD: The multi-category large-scale dataset for maritime object detection","volume":"75","author":"Sun","year":"2023","journal-title":"Comput. Mater. Contin."},{"key":"ref_4","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/5254.708428","article-title":"Support vector machines","volume":"13","author":"Hearst","year":"1998","journal-title":"IEEE Intell. Syst. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3296495","DOI":"10.1155\/2022\/3296495","article-title":"Detection of marine oil spills based on HOG feature and SVM classifier","volume":"2022","author":"Li","year":"2022","journal-title":"J. Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1080\/03088839.2021.1875141","article-title":"Ship detention prediction via feature selection scheme and support vector machine (SVM)","volume":"49","author":"Wu","year":"2022","journal-title":"Marit. Policy Manag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Karna, H., Braovi\u0107, M., Gudelj, A., and Buli\u010di\u0107, K. (2025). Artificial intelligence-based prediction model for maritime vessel type identification. Information, 16.","DOI":"10.3390\/info16050367"},{"key":"ref_10","first-page":"41","article-title":"Ship detection with improved convolutional neural network","volume":"41","author":"Wang","year":"2018","journal-title":"Navig. China"},{"key":"ref_11","first-page":"101297","article-title":"Faster R\u2013CNN, RetinaNet and Single Shot Detector in different ResNet backbones for marine vessel detection using cross polarization C-band SAR imagery","volume":"36","author":"Yaltarez","year":"2024","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Cai, J., Du, S., Lu, C., Xiao, B., and Wu, M. (2023, January 8\u201311). Obstacle detection of unmanned surface vessel based on faster RCNN. Proceedings of the 2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS), Wuhan, China.","DOI":"10.1109\/ICPS58381.2023.10128076"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, N., Tang, L., and Wu, W. (2022, January 7\u20139). Marine Vessel Detection in Sea Fog Environment Based on SSD. Proceedings of the International Conference on Sensor Systems and Software, Dalian, China.","DOI":"10.1007\/978-3-031-34899-0_4"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_15","first-page":"1187","article-title":"A real-time detection method for weak and small moving ships at sea","volume":"44","author":"Weina","year":"2021","journal-title":"J. Hefei Univ. Technol. (Nat. Sci. Ed.)"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Gong, H., Li, H., Xu, K., and Zhang, Y. (2019, January 22\u201324). Object detection based on improved YOLOv3-tiny. Proceedings of the 2019 Chinese Automation Congress (CAC), Hangzhou, China.","DOI":"10.1109\/CAC48633.2019.8996750"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, T., Pang, B., Ai, S., and Zhang, X. (2020). Study on visual detection algorithm of sea surface targets based on improved YOLOv3. Sensors, 20.","DOI":"10.3390\/s20247263"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Fu, H.X., Song, G.Q., and Wang, Y.C. (2021). Improved YOLOv4 marine target detection combined with CBAM. Symmetry, 13.","DOI":"10.3390\/sym13040623"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1049\/ipr2.12432","article-title":"Fast ship detection based on lightweight YOLOv5 network","volume":"16","author":"Zheng","year":"2022","journal-title":"IET Image Process"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lin, F., Hou, T., Jin, Q., and Zhang, Y. (2021). Improved YOLO based detection algorithm for floating debris in waterway. Entropy, 23.","DOI":"10.3390\/e23091111"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sohan, M., Sai Ram, T., and Rami Reddy, C.V. (2024, January 18\u201320). A review on YOLOv8 and its advancements. Proceedings of the International Conference on Data Intelligence and Cognitive Informatics, Tirunelveli, India.","DOI":"10.1007\/978-981-99-7962-2_39"},{"key":"ref_22","unstructured":"Narayanan, M. (2023). Senetv2: Aggregated dense layer for channelwise and global representations. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zagoruyko, S., and Komodakis, N. (2016). Wide residual networks. arXiv.","DOI":"10.5244\/C.30.87"},{"key":"ref_25","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_26","unstructured":"Yang, L., Zhang, R.-Y., Li, L., and Xie, X. (2021, January 18\u201324). SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks. Proceedings of the 38th International Conference on Machine Learning, PMLR, Virtual."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Qian, J., Lin, J., Bai, D., Xu, R., and Lin, H. (2023). Omni-Dimensional Dynamic Convolution Meets Bottleneck Transformer: A Novel Improved High Accuracy Forest Fire Smoke Detection Model. Forests, 14.","DOI":"10.3390\/f14040838"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Tian, Z., Yang, F., and Qin, D. (2023). An Improved New YOLOv7 Algorithm for Detecting Building Air Conditioner External Units from Street View Images. Sensors, 23.","DOI":"10.3390\/s23229118"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bai, W., Zhao, J., Dai, C., Zhang, H., Zhao, L., Ji, Z., and Ganchev, I. (2023). Two Novel Models for Traffic Sign Detection Based on YOLOv5s. Axioms, 12.","DOI":"10.3390\/axioms12020160"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, J., Tian, Y., Chen, J., and Wang, H. (2023). Rock Crack Recognition Technology Based on Deep Learning. Sensors, 23.","DOI":"10.3390\/s23125421"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Ni, Q. (2023). A Novel Weld-Seam Defect Detection Algorithm Based on the S-YOLO Model. Axioms, 12.","DOI":"10.3390\/axioms12070697"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"121036","DOI":"10.1016\/j.eswa.2023.121036","article-title":"An improved lightweight small object detection framework applied to real-time autonomous driving","volume":"234","author":"Mahaur","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). EfficientDet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bodla, N., Singh, B., Chellappa, R., and Davis, L.S. (2017, January 22\u201329). Soft-NMS\u2013Improving object detection with one line of code. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.593"},{"key":"ref_36","unstructured":"Powers, D.M. (2020). Evaluation: From precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Boyd, K., Eng, K.H., and Page, C.D. (2013, January 23\u201327). Area under the precision-recall curve: Point estimates and confidence intervals. Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECMLPKDD), Prague, Czech Republic.","DOI":"10.1007\/978-3-642-40994-3_29"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1023\/A:1016328200723","article-title":"Vision and the atmosphere","volume":"48","author":"Narasimhan","year":"2002","journal-title":"Int. J. Comput. Vis."},{"key":"ref_39","unstructured":"(2022, January 28). Yolo-Project. 6_class_final Dataset. Roboflow Universe. Roboflow, Available online: https:\/\/universe.roboflow.com\/yolo-project\/6_class_final."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/7\/667\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:56:28Z","timestamp":1760032588000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/7\/667"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,22]]},"references-count":39,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2025,7]]}},"alternative-id":["e27070667"],"URL":"https:\/\/doi.org\/10.3390\/e27070667","relation":{},"ISSN":["1099-4300"],"issn-type":[{"value":"1099-4300","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,22]]}}}