{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T05:21:32Z","timestamp":1769059292817,"version":"3.49.0"},"reference-count":36,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T00:00:00Z","timestamp":1768867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Anhui Province University Key Science and Technology Project","award":["2024AH053415"],"award-info":[{"award-number":["2024AH053415"]}]},{"name":"Anhui Province University Major Science and Technology Project","award":["2024AH040229"],"award-info":[{"award-number":["2024AH040229"]}]},{"name":"Talent Research Initiation Fund Project of Tongling University","award":["2024tlxyrc019"],"award-info":[{"award-number":["2024tlxyrc019"]}]},{"name":"Tongling University School-Level Scientific Research Project","award":["2024tlxyptZD07"],"award-info":[{"award-number":["2024tlxyptZD07"]}]},{"name":"Tongling University School-Level Scientific Research Plan Project","award":["2023tlxyptZD04"],"award-info":[{"award-number":["2023tlxyptZD04"]}]},{"name":"The University Synergy Innovation Program of Anhui Province","award":["GXXT-2023-050"],"award-info":[{"award-number":["GXXT-2023-050"]}]},{"name":"Tongling City Science and Technology Major Special Project","award":["200401JB004"],"award-info":[{"award-number":["200401JB004"]}]},{"name":"Anhui Province University Key Science and Technology Project","award":["2024AH053415"],"award-info":[{"award-number":["2024AH053415"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>The proliferation of smart devices and the Internet of Things (IoT) has led to massive data generation, particularly in complex domains such as aerospace. Cloud computing provides essential scalability and advanced analytics for processing these vast datasets. However, relying solely on the cloud introduces significant challenges, including high latency, network congestion, and substantial bandwidth costs, which are critical for real-time on-orbit spacecraft services. Cloud-edge Internet of Things (cloud-edge IoT) computing emerges as a promising architecture to mitigate these issues by pushing computation closer to the data source. This paper proposes an improved YOLOV8-based model specifically designed for edge computing scenarios within a cloud-edge IoT framework. By integrating the Cross Stage Partial Spatial Pyramid Pooling Fast (CSPPF) module and the WDIOU loss function, the model achieves enhanced feature extraction and localization accuracy without significantly increasing computational cost, making it suitable for deployment on resource-constrained edge devices. Meanwhile, by processing image data locally at the edge and transmitting only the compact segmentation results to the cloud, the system effectively reduces bandwidth usage and supports efficient cloud-edge collaboration in IoT-based spacecraft monitoring systems. Experimental results show that, compared to the original YOLOV8 and other mainstream models, the proposed model demonstrates superior accuracy and instance segmentation performance at the edge, validating its practicality in cloud-edge IoT environments.<\/jats:p>","DOI":"10.3390\/fi18010059","type":"journal-article","created":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T14:57:58Z","timestamp":1768921078000},"page":"59","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Edge-Enhanced YOLOV8 for Spacecraft Instance Segmentation in Cloud-Edge IoT Environments"],"prefix":"10.3390","volume":"18","author":[{"given":"Ming","family":"Chen","sequence":"first","affiliation":[{"name":"School of Mathematics and Computer Science, Tongling University, Tongling 244061, China"},{"name":"Anhui Engineering Research Center of Intelligent Manufacturing of Copper-Based Materials, Tongling University, Tongling 244061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjie","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Tongling University, Tongling 244061, China"},{"name":"Anhui Engineering Research Center of Intelligent Manufacturing of Copper-Based Materials, Tongling University, Tongling 244061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanfei","family":"Niu","sequence":"additional","affiliation":[{"name":"College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Qi","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Tongling University, Tongling 244061, China"},{"name":"Anhui Engineering Research Center of Intelligent Manufacturing of Copper-Based Materials, Tongling University, Tongling 244061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fucheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Tongling University, Tongling 244061, China"},{"name":"Anhui Engineering Research Center of Intelligent Manufacturing of Copper-Based Materials, Tongling University, Tongling 244061, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,20]]},"reference":[{"key":"ref_1","unstructured":"Chu, G.L. (2015). Study on the Key Technologies of Automatic Identification for Cooperative Target on Spacecraft. [Ph.D. Thesis, University of Chinese Academy of Sciences (Changchun Institute of Optics, Fine Mechanics and Physics)]."},{"key":"ref_2","first-page":"805","article-title":"A Review of On-Orbit Servicing","volume":"28","author":"Cui","year":"2007","journal-title":"J. Astronaut."},{"key":"ref_3","first-page":"70","article-title":"Development of Space Rendezvous and Docking Technology in Past 40 Years","volume":"16","author":"Ling","year":"2007","journal-title":"Spacecr. Eng."},{"key":"ref_4","unstructured":"Yaseen, M. (2023). What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector. arXiv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1109\/JIOT.2016.2579198","article-title":"Edge computing: Vision and challenges","volume":"3","author":"Shi","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2322","DOI":"10.1109\/COMST.2017.2745201","article-title":"A survey on mobile edge computing: The communication perspective","volume":"19","author":"Mao","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1738","DOI":"10.1109\/JPROC.2019.2918951","article-title":"Edge intelligence: Paving the last mile of artificial intelligence with edge computing","volume":"107","author":"Zhou","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_8","first-page":"810","article-title":"Survey of Research on Instance Segmentation Methods","volume":"17","author":"Huang","year":"2023","journal-title":"J. Front. Comput. Sci. Technol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wu, T., Yang, X., Song, B., Wang, N., Gao, X., Kuang, L., Nan, X., Chen, Y., and Yang, D. (2019). T-SCNN: A two-stage convolutional neural network for space target recognition. IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, IEEE.","DOI":"10.1109\/IGARSS.2019.8900185"},{"key":"ref_10","unstructured":"Armstrong, W., Draktontaidis, S., and Lui, N. (2021). Semantic Image Segmentation of Imagery of Unmanned Spacecraft Using Synthetic Data, IEEE. Technical Report."},{"key":"ref_11","unstructured":"Hariharan, B., Arbel\u00e1ez, P., Girshick, R., and Malik, J. (2014). Simultaneous detection and segmentation. Computer Vision\u2013ECCV 2014: 13th European Conference, Proceedings, Part VII 13, Zurich, Switzerland, 6\u201312 September 2014, Springer International Publishing."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Arbel\u00e1ez, P., Pont-Tuset, J., Barron, J.T., Marques, F., and Malik, J. (2014, January 23\u201328). Multiscale combinatorial grouping. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.49"},{"key":"ref_13","unstructured":"Dai, J., He, K., Li, Y., Ren, S., and Sun, J. (2016). Instance-sensitive fully convolutional networks. Computer Vision\u2013ECCV 2016: 14th European Conference, Proceedings, Part VI 14, Amsterdam, The Netherlands, 11\u201314 October 2016, Springer International Publishing."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol. (TIST)"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_16","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017). Mask R-CNN. IEEE International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_18","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems (NIPS), Neural Information Processing Systems Foundation, Inc."},{"key":"ref_19","unstructured":"Gao, N., Shan, Y., Wang, Y., Zhao, X., Yu, Y., Yang, M., and Huang, K. (November, January 27). Ssap: Single-shot instance segmentation with affinity pyramid. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ke, L., Danelljan, M., Li, X., Tai, Y.W., Tang, C.K., and Yu, F. (2022, January 18\u201324). Mask transfiner for high-quality instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00437"},{"key":"ref_21","unstructured":"Bolya, D., Zhou, C., Xiao, F., and Lee, Y.J. (November, January 27). Yolact: Real-time instance segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8275","DOI":"10.1007\/s00521-021-05978-9","article-title":"Poly-YOLO: Higher speed, more precise detection and instance segmentation for YOLOv3","volume":"34","author":"Hurtik","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"He, J., Li, P., Geng, Y., and Xie, X. (2023, January 17\u201324). Fastinst: A simple query-based model for real-time instance segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.02266"},{"key":"ref_24","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024, January 9\u201315). YOLOv10: Real-Time End-to-End Object Detection. Proceedings of the 38th International Conference on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"107510","DOI":"10.1016\/j.marenvres.2025.107510","article-title":"A novel underwater Holothurians monitoring system using consumer-grade amphibious UAV with Mamba-based Super-Resolution Reconstruction and enhanced YOLOv10","volume":"212","author":"Zhao","year":"2025","journal-title":"Mar. Environ. Res."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"27043","DOI":"10.1038\/s41598-025-12468-8","article-title":"Automated non-PPE detection on construction sites using YOLOv10 and transformer architectures for surveillance and body worn cameras with benchmark datasets","volume":"15","author":"Wang","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_27","first-page":"27","article-title":"Enhancing real-time instance segmentation for plant disease detection with improved YOLOv8-Seg algorithm","volume":"16","author":"Ammar","year":"2024","journal-title":"Int. J. Inf. Technol. Secur."},{"key":"ref_28","unstructured":"Ma, J., Li, F.F., and Wang, B. (2024). U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103324","DOI":"10.1016\/j.ecoinf.2025.103324","article-title":"Mamba-based super-resolution and semi-supervised YOLOv10 for freshwater mussel detection using acoustic video camera: A case study at Lake Izunuma, Japan","volume":"90","author":"Zhao","year":"2025","journal-title":"Ecol. Inform."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"You, S., Li, B., Chen, Y., Ren, Z., Liu, Y., Wu, Q., and Zhao, F. (2025). Rose-Mamba-YOLO: An enhanced framework for efficient and accurate greenhouse rose monitoring. Front. Plant Sci., 16.","DOI":"10.3389\/fpls.2025.1607582"},{"key":"ref_31","unstructured":"Chen, M., Chen, W.J., Niu, Y.F., Qi, P., and Wang, F.C. (2026, January 13). Yolov8_Pro_Cssp. [Computer Software, GitHub Repository]. Available online: https:\/\/github.com\/cehndashuai\/yolov8_pro_cssp.git."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dung, H.A., Chen, B., and Chin, T.J. (2021, January 19\u201325). A spacecraft dataset for detection, segmentation and parts recognition. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00229"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1108","DOI":"10.1109\/TPAMI.2020.3014297","article-title":"YOLACT++: Better Real-time Instance Segmentation","volume":"44","author":"Bolya","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","unstructured":"(2026, January 13). Ultralytics. Available online: https:\/\/github.com\/ultralytics\/yolov5."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Yeh, I.H., and Mark Liao, H.Y. (2024). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. European Conference on Computer Vision, Springer Nature.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_36","unstructured":"Tian, Y., Ye, Q., and Doermann, D. (2025). YOLOv12: Attention-Centric Real-Time Object Detectors. arXiv."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/18\/1\/59\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T14:10:52Z","timestamp":1769004652000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/18\/1\/59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,20]]},"references-count":36,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["fi18010059"],"URL":"https:\/\/doi.org\/10.3390\/fi18010059","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,20]]}}}