{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T14:11:14Z","timestamp":1765807874356,"version":"3.48.0"},"reference-count":37,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Systems"],"abstract":"<jats:p>This paper is dedicated to solving the environmental perception system problem of unmanned surface vehicles (USVs) experiencing adverse sea conditions and complex mission scenarios. First, the functionalities and characteristics of each subsystem in the USV environmental perception system under different mission scenarios are analyzed, and an efficient and stable environmental perception system is designed. Second, the static and dynamic characteristics of the sea\u2013sky line are investigated, along with the impacts on each subsystem of the environmental perception system when the USV experiences six-degree-of-freedom motion on the sea surface. Based on the above analysis, a sea\u2013sky line detection method based on the radar\u2013electro-optical system is designed. This method utilizes the features of the radar and electro-optical subsystems to redefine the region of interest, effectively suppressing interference from non-sea\u2013sky line edges, thereby improving detection efficiency and accuracy. Furthermore, a sea\u2013sky line-based target detection algorithm is proposed, which confines the search area to the vicinity of the detected sea\u2013sky line, significantly reducing false detections caused by sea clutter and noise. Sea trials demonstrate that the proposed method enhances the accuracy and real-time performance of USV environmental perception. The proposed systematic approach offers a practical solution for improving the robustness of USV environmental perception in complex marine environments. Sea trials have shown that the method improves the effectiveness of target information by 3.61%.<\/jats:p>","DOI":"10.3390\/systems13121123","type":"journal-article","created":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T13:32:37Z","timestamp":1765805557000},"page":"1123","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Environmental Perception Method for Unmanned Surface Vehicles Based on Sea\u2013Sky Line Detection"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6216-4893","authenticated-orcid":false,"given":"Qingze","family":"Yu","sequence":"first","affiliation":[{"name":"Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China"}]},{"given":"Ronghua","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China"}]},{"given":"Guangnian","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Maritime and Transportation, Ningbo University, Ningbo 315832, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2358","DOI":"10.1109\/TVT.2021.3136670","article-title":"Autonomous Pilot of Unmanned Surface Vehicles: Bridging Path Planning and Tracking","volume":"71","author":"Wang","year":"2022","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3034","DOI":"10.1109\/TNNLS.2020.3009214","article-title":"Reinforcement Learning-Based Optimal Tracking Control of an Unknown Unmanned Surface Vehicle","volume":"32","author":"Wang","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"15181","DOI":"10.1109\/JIOT.2025.3528049","article-title":"Adaptive Control Scheme for USV Trajectory Tracking Under Complex Environmental Dis-turbances via Deep Reinforcement Learning","volume":"12","author":"Zhou","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.arcontrol.2016.04.018","article-title":"Unmanned surface vehicles: An overview of developments and challenges","volume":"41","author":"Liu","year":"2016","journal-title":"Annu. Rev. Control"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"24518","DOI":"10.1109\/JIOT.2025.3555243","article-title":"Obstacle Avoidance for Unmanned Surface Vehicle by Null-Space Guidance Vector Field with Deep Reinforcement Learning","volume":"12","author":"Yao","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_6","first-page":"1","article-title":"Real-Time Volumetric Perception for Unmanned Surface Vehicles Through Fusion of Radar and Camera","volume":"73","author":"Xu","year":"2024","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Baseri, R.M., and Seif, M.S. (2025). Fuzzy-Adaptive Backstepping Dynamic Sliding Mode Control strategy for Unmanned Surface Vehicles. Iran. J. Sci. Technol. Trans. Electr. Eng., 1\u201311.","DOI":"10.1007\/s40998-025-00842-1"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1109\/LCSYS.2025.3578918","article-title":"Data-Based Encryption Iterative Learning Heading Control for Unmanned Surface Vehicles","volume":"9","author":"Chen","year":"2025","journal-title":"IEEE Control Syst. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1007\/s00773-025-01070-2","article-title":"Global\u2013local hierarchical path planning method for unmanned surface vehicles based on dynamic constraints","volume":"30","author":"Dong","year":"2025","journal-title":"J. Mar. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"15274","DOI":"10.1109\/JIOT.2025.3530534","article-title":"RL-Based USV Path Planning Under the Marine Multimodal Features Considerations","volume":"12","author":"Lin","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lv, Z., Wang, X., Wang, G., Xing, X., Lv, C., and Yu, F. (2025). Unmanned Surface Vessels in Marine Surveillance and Management: Advances in Communication, Navigation, Control, and Data-Driven Research. J. Mar. Sci. Eng., 13.","DOI":"10.3390\/jmse13050969"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bruzzone, G., Bruzzone, G., Bibuli, M., and Caccia, M. (2011, January 6\u20139). Autonomous Mine Hunting Mission for the Charlie USV. Proceedings of the 2011 IEEE-Oceans Spain, Santander, Spain.","DOI":"10.1109\/Oceans-Spain.2011.6003469"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ohta, Y., Yoshida, H., Ishibashi, S., Sugesawa, M., Fan, F.H., and Tanaka, K. (2016, January 19\u201323). Seabed resource exploration performed by AUV \u201cYumeiruka\u201d. Proceedings of the Oceans 2016 MTS\/IEEE Monterey, Monterey, CA, USA.","DOI":"10.1109\/OCEANS.2016.7761122"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yu, Q., Su, Y., and Zhang, R. (2023). Object Extraction Algorithm for the First-Frame Image of Unmanned Surface Vehicles Based on a Radar-Photoelectric System. J. Mar. Sci. Eng., 11.","DOI":"10.3390\/jmse11020344"},{"key":"ref_15","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_16","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. arXiv."},{"key":"ref_17","unstructured":"Guina, M., Gong, H., Niu, Z., and Lu, J. (2014). A Fast Sea-Level Line Extraction and Object Detection Method for Infrared Sea Image. International Symposium on Optoelectronic Technology and Application 2014: Infrared Technology and Applications, SPIE."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ma, T., Ma, J., and Fu, W. (2016, January 10\u201311). Sea-Sky Line Extraction with Linear Fitting Based on Line Segment Detection. Proceedings of the 2016 9th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China.","DOI":"10.1109\/ISCID.2016.1019"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lu, J., Ren, J., Lu, Y., Yuan, X., and Wang, C. (2006, January 16\u201318). A Modified Canny Algorithm for Detecting Sky-Sea Line in Infrared Images. Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications, Jian, China.","DOI":"10.1109\/ISDA.2006.253848"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wang, B., Su, Y., and Wan, L. (2016). A Sea-Sky Line Detection Method for Unmanned Surface Vehicles Based on Gradient Saliency. Sensors, 16.","DOI":"10.3390\/s16040543"},{"key":"ref_21","first-page":"1031","article-title":"A Sea-Sky Line Identification Algorithem Based on Shearlets for Infrared Image","volume":"846","author":"Zou","year":"2014","journal-title":"Adv. Mater. Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Yang, L., Zhang, P., Huang, L., and Wu, L. (2021, January 17\u201319). Sea-sky-line Detection Based on Improved YOLOv5 Algorithm. Proceedings of the 2021 IEEE 2nd International Conference on Information Technology, Big Data and Artificial Intelligence (ICIBA), Chongqing, China.","DOI":"10.1109\/ICIBA52610.2021.9688042"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1109\/TMM.2020.3008028","article-title":"Learning Deep Multi-Level Similarity for Thermal Infrared Object Tracking","volume":"23","author":"Liu","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103448","DOI":"10.1016\/j.artint.2020.103448","article-title":"Multiple object tracking: A literature review","volume":"293","author":"Luo","year":"2021","journal-title":"Artif. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4225","DOI":"10.1109\/TGRS.2019.2961807","article-title":"Identification of Rain and Low-Backscatter Regions in X-Band Marine Radar Images: An Unsupervised Approach","volume":"58","author":"Chen","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"867","DOI":"10.1007\/s13344-016-0056-0","article-title":"Radar-based collision avoidance for unmanned surface vehicles","volume":"30","author":"Zhuang","year":"2016","journal-title":"China Ocean Eng."},{"key":"ref_27","first-page":"104","article-title":"Deep Affinity Network for Multiple Object Tracking","volume":"43","author":"Sun","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.neucom.2019.11.023","article-title":"Deep learning in video multi-object tracking: A survey","volume":"381","author":"Ciaparrone","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"112257","DOI":"10.1016\/j.oceaneng.2022.112257","article-title":"USV compliant obstacle avoidance based on dynamic two ship domains","volume":"262","author":"Sun","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.oceaneng.2015.01.008","article-title":"Path planning algorithm for unmanned surface vehicle formations in a practical maritime environment","volume":"97","author":"Liu","year":"2015","journal-title":"Ocean Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"107043","DOI":"10.1016\/j.oceaneng.2020.107043","article-title":"The review unmanned surface vehicle path planning: Based on multi-modality constraint","volume":"200","author":"Zhou","year":"2020","journal-title":"Ocean Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1080\/21655979.2020.1778913","article-title":"Edge detection algorithm of cancer image based on deep learning","volume":"11","author":"Li","year":"2020","journal-title":"Bioengineered"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"33240","DOI":"10.1109\/ACCESS.2019.2902579","article-title":"An Efficient Edge Detection Approach to Provide Better Edge Connectivity for Image Analysis","volume":"7","author":"Mittal","year":"2019","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"106452","DOI":"10.1016\/j.asoc.2020.106452","article-title":"Fuzzy based image edge detection algorithm for blood vessel detection in retinal images","volume":"94","author":"Orujov","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1007\/s40815-020-01030-5","article-title":"Image Edge Detection: A New Approach Based on Fuzzy Entropy and Fuzzy Divergence","volume":"23","author":"Versaci","year":"2021","journal-title":"Int. J. Fuzzy Syst."},{"key":"ref_36","first-page":"41","article-title":"Image steganography based on Canny edge detection, dilation operator and hybrid coding","volume":"41","author":"Gaurav","year":"2018","journal-title":"J. Inf. Secur. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kanchanatripop, P., and Zhang, D. (2020). Adaptive Image Edge Extraction Based on Discrete Algorithm and Classical Canny Operator. Symmetry, 12.","DOI":"10.3390\/sym12111749"}],"container-title":["Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/12\/1123\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T14:02:37Z","timestamp":1765807357000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-8954\/13\/12\/1123"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,15]]},"references-count":37,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["systems13121123"],"URL":"https:\/\/doi.org\/10.3390\/systems13121123","relation":{},"ISSN":["2079-8954"],"issn-type":[{"value":"2079-8954","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,15]]}}}