{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T12:30:35Z","timestamp":1790253035270,"version":"4.1.0"},"reference-count":47,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,20]],"date-time":"2026-06-20T00:00:00Z","timestamp":1781913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005270","name":"Fujian Provincial Department of Science and Technology","doi-asserted-by":"crossref","award":["2021N5004"],"award-info":[{"award-number":["2021N5004"]}],"id":[{"id":"10.13039\/501100005270","id-type":"DOI","asserted-by":"crossref"}]},{"award":["2021N5004"],"award-info":[{"award-number":["2021N5004"]}],"id":[{"id":"https:\/\/ror.org\/00rgzpv08","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016078","name":"Fuzhou Science and Technology Bureau","doi-asserted-by":"crossref","award":["2024-G-014"],"award-info":[{"award-number":["2024-G-014"]}],"id":[{"id":"10.13039\/100016078","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biology"],"abstract":"<jats:p>Accurate and efficient detection of cyanobacteria in microscopic images is important for automated water-quality monitoring, but remains challenging because of complex aquatic backgrounds, large scale variation, and uneven sample quality. This study proposes an adaptive multi-scale fusion enhanced RT-DETR framework for cyanobacteria detection. The baseline RT-DETR-R18 is improved by incorporating the SeFaster module for efficient feature extraction, the high-level screening-feature fusion pyramid network for semantic-guided multi-scale fusion, and the Wise-IoU loss for more stable localization learning under mixed-quality samples. Experiments on the reorganized EMDS-7 dataset show that the proposed method achieved 79.05% mAP@0.5, 66.03% mAP@0.5:0.95, 16.31 M parameters, 54.6 G FLOPs, and 70.85 FPS. The proposed model also obtained the highest mAP@0.5 across the seven cyanobacteria categories. Moreover, cross-dataset evaluations further suggest the stability and transferability of the model. These results indicate that the proposed framework demonstrates potential for effective cyanobacteria detection in microscopic images with a good balance between detection accuracy and computational efficiency.<\/jats:p>","DOI":"10.3390\/biology15120970","type":"journal-article","created":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T03:06:34Z","timestamp":1782875194000},"page":"970","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Adaptive Multi-Scale Fusion Enhanced RT-DETR for Efficient Cyanobacteria Detection in Microscopic Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Jianxing","family":"Li","sequence":"first","affiliation":[{"name":"School of Electrical and Information Engineering, Fujian University of Technology, Fuzhou 350118, China"},{"name":"Fuzhou Industrial Integration Automation Technology Innovation Center, Fuzhou 350118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shizhi","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Fujian University of Technology, Fuzhou 350118, China"},{"name":"Fuzhou Industrial Integration Automation Technology Innovation Center, Fuzhou 350118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2317-6714","authenticated-orcid":false,"given":"Kan","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Fujian University of Technology, Fuzhou 350118, China"},{"name":"Fuzhou Industrial Integration Automation Technology Innovation Center, Fuzhou 350118, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1126\/science.1245276","article-title":"Blooms Bite the Hand That Feeds Them","volume":"342","author":"Paerl","year":"2013","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1146\/annurev-environ-112320-081653","article-title":"Harmful Cyanobacterial Blooms: Biological Traits, Mechanisms, Risks, and Control Strategies","volume":"48","author":"Song","year":"2023","journal-title":"Annu. Rev. Environ. Resour."},{"key":"ref_3","first-page":"147","article-title":"Cyanobacterial bloom hazards and prevention strategies","volume":"16","author":"Bi","year":"2025","journal-title":"Rural Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"606","DOI":"10.1007\/s40726-024-00322-w","article-title":"Cyanobacterial Blooms in Environmental Water: Causes and Solutions","volume":"10","author":"Lakshmikandan","year":"2024","journal-title":"Curr. Pollut. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/j.envint.2016.02.026","article-title":"Acute Animal and Human Poisonings from Cyanotoxin Exposure\u2014A Review of the Literature","volume":"91","author":"Wood","year":"2016","journal-title":"Environ. Int."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2429","DOI":"10.1007\/s00204-019-02524-4","article-title":"Global Geographical and Historical Overview of Cyanotoxin Distribution and Cyanobacterial Poisonings","volume":"93","author":"Tokodi","year":"2019","journal-title":"Arch. Toxicol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1038\/s41522-025-00771-1","article-title":"Temporal Dynamics, Microdiversity, and Ecological Functions of Viral Communities during Cyanobacterial Blooms in Lake Taihu","volume":"11","author":"Chen","year":"2025","journal-title":"npj Biofilms Microbiomes"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhang, W., Liu, J., Xiao, Y., Zhang, Y., Yu, Y., Zheng, Z., Liu, Y., and Li, Q. (2022). The Impact of Cyanobacteria Blooms on the Aquatic Environment and Human Health. Toxins, 14.","DOI":"10.3390\/toxins14100658"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"8073","DOI":"10.1007\/s00253-014-5951-9","article-title":"Methods to Detect Cyanobacteria and Their Toxins in the Environment","volume":"98","author":"Moreira","year":"2014","journal-title":"Appl. Microbiol. Biotechnol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Saleem, F., Jiang, J.L., Atrache, R., Paschos, A., Edge, T.A., and Schellhorn, H.E. (2023). Cyanobacterial Algal Bloom Monitoring: Molecular Methods and Technologies for Freshwater Ecosystems. Microorganisms, 11.","DOI":"10.3390\/microorganisms11040851"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"128418","DOI":"10.1016\/j.biortech.2022.128418","article-title":"Microalgae Identification: Future of Image Processing and Digital Algorithm","volume":"369","author":"Chong","year":"2023","journal-title":"Bioresour. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"102187","DOI":"10.1016\/j.hal.2022.102187","article-title":"Comparing Microscopy and DNA Metabarcoding Techniques for Identifying Cyanobacteria Assemblages across Hundreds of Lakes","volume":"113","author":"MacKeigan","year":"2022","journal-title":"Harmful Algae"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mosleh, M.A., Manssor, H., Malek, S., Milow, P., and Salleh, A. (2012). A Preliminary Study on Automated Freshwater Algae Recognition and Classification System. BMC Bioinform., 13.","DOI":"10.1186\/1471-2105-13-S17-S25"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1007\/s12665-013-2455-3","article-title":"Identification of Algal Blooms Based on Support Vector Machine Classification in Haizhou Bay, East China Sea","volume":"71","author":"Xu","year":"2014","journal-title":"Environ. Earth Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"103651","DOI":"10.1016\/j.algal.2024.103651","article-title":"Identify and Segment Microalgae in Complex Backgrounds with Improved YOLO","volume":"82","author":"Yang","year":"2024","journal-title":"Algal Res."},{"key":"ref_16","unstructured":"Liang, Z., Liu, T., Li, L., Zhu, Y., and Lu, C. (2025). Rapid Detection and Recognition of Algae Based on an Improved EfficientDet Model. Comput. Eng., 1\u201313."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2602","DOI":"10.1049\/ipr2.13119","article-title":"Microalgae Detection Based on Improved YOLO V5","volume":"18","author":"Duan","year":"2024","journal-title":"IET Image Process."},{"key":"ref_18","first-page":"257","article-title":"Detection Algorithm of Planktonic Algae Based on Improved YOLOv3","volume":"60","author":"Chu","year":"2023","journal-title":"Laser Optoelectron. Prog."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ding, G., Shi, Y., Liu, Z., Wang, Y., Yao, Z., Zhou, D., Zhu, X., and Li, Y. (2025). FE-YOLO: An Efficient Deep Learning Model Based on Feature-Enhanced YOLOv7 for Microalgae Identification and Detection. Biomimetics, 10.","DOI":"10.3390\/biomimetics10010062"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"127565","DOI":"10.1016\/j.eswa.2025.127565","article-title":"An Approach to Microalgae Identification Based on Joint Optimization Model of Slicing and Detection","volume":"280","author":"Junfei","year":"2025","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, D., Wang, P., Cheng, Y., and Bi, H. (2022). An Improved Algae-YOLO Model Based on Deep Learning for Object Detection of Ocean Microalgae Considering Aquacultural Lightweight Deployment. Front. Mar. Sci., 9.","DOI":"10.3389\/fmars.2022.1070638"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"121274","DOI":"10.1016\/j.jenvman.2024.121274","article-title":"Automated Identification of Toxigenic Cyanobacterial Genera for Water Quality Control Purposes","volume":"362","author":"Kianian","year":"2024","journal-title":"J. Environ. Manag."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"107917","DOI":"10.1016\/j.compbiomed.2024.107917","article-title":"Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases","volume":"170","author":"Chen","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_24","unstructured":"Tong, Z., Chen, Y., Xu, Z., and Yu, R. (2023). Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yang, H., Li, C., Zhao, X., Cai, B., Zhang, J., Ma, P., Zhao, P., Chen, A., Jiang, T., and Sun, H. (2023). EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluation. Front. Microbiol., 14.","DOI":"10.3389\/fmicb.2023.1084312"},{"key":"ref_26","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_27","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_28","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., Bischof, H., Brox, T., and Frahm, J.-M. (2020). End-to-End Object Detection with Transformers. Proceedings of the Computer Vision\u2014ECCV 2020, Springer International Publishing.","DOI":"10.1007\/978-3-030-58589-1"},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024, January 16\u201322). DETRs Beat YOLOs on Real-Time Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, J., Kao, S., He, H., Zhuo, W., Wen, S., Lee, C.-H., and Chan, S.-H.G. (2023, January 17\u201324). Run, Don\u2019t Walk: Chasing Higher FLOPS for Faster Neural Networks. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201322). Squeeze-and-Excitation Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Varghese, R., and Sambath, M. (2024, January 18\u201319). YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness. Proceedings of the 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS), Chennai, India.","DOI":"10.1109\/ADICS58448.2024.10533619"},{"key":"ref_35","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_36","unstructured":"Khanam, R., and Hussain, M. (2024). YOLOv11: An Overview of the Key Architectural Enhancements. arXiv."},{"key":"ref_37","first-page":"78433","article-title":"YOLOv12: Attention-Centric Real-Time Object Detectors","volume":"38","author":"Tian","year":"2026","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","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_39","unstructured":"Sapkota, R., Cheppally, R.H., Sharda, A., and Karkee, M. (2025). YOLO26: Key Architectural Enhancements and Performance Benchmarking for Real-Time Object Detection. arXiv."},{"key":"ref_40","unstructured":"Peng, Y., Li, H., Wu, P., Zhang, Y., Sun, X., and Wu, F. (2024, January 4). D-FINE: Redefine Regression Task of DETRs as Fine-Grained Distribution Refinement. Proceedings of the International Conference on Learning Representations 2025 (ICLR 2025), Singapore."},{"key":"ref_41","unstructured":"Robinson, I., Robicheaux, P., Popov, M., Ramanan, D., and Peri, N. (2025). RF-DETR: Neural Architecture Search for Real-Time Detection Transformers. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V.N. (2018, January 12\u201315). Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00097"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhou, S., Jiang, J., Hong, X., Fu, P., and Yan, H. (2023). Vision Meets Algae: A Novel Way for Microalgae Recognization and Health Monitor. Front. Mar. Sci., 10.","DOI":"10.3389\/fmars.2023.1105545"},{"key":"ref_44","unstructured":"Sun, M., Jiang, J., Yang, Z., Kong, S., Qi, J., Shang, J., Luo, S., Sun, W., Wang, T., and Wang, Y. (2025). VisAlgae 2023: A Dataset and Challenge for Algae Detection in Microscopy Images. arXiv."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Mehdizadeh Allaf, M., and Peerhossaini, H. (2022). Cyanobacteria: Model Microorganisms and Beyond. Microorganisms, 10.","DOI":"10.3390\/microorganisms10040696"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T. (2014). Microsoft COCO: Common Objects in Context. Proceedings of the Computer Vision\u2014ECCV 2014, Springer International Publishing.","DOI":"10.1007\/978-3-319-10590-1"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s44211-021-00013-2","article-title":"A Review of Microscopic Cell Imaging and Neural Network Recognition for Synergistic Cyanobacteria Identification and Enumeration","volume":"38","author":"Vaughan","year":"2022","journal-title":"Anal. Sci."}],"container-title":["Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-7737\/15\/12\/970\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T04:16:36Z","timestamp":1783052196000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-7737\/15\/12\/970"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,20]]},"references-count":47,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["biology15120970"],"URL":"https:\/\/doi.org\/10.3390\/biology15120970","relation":{},"ISSN":["2079-7737"],"issn-type":[{"value":"2079-7737","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,20]]}}}