{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T00:02:20Z","timestamp":1774051340264,"version":"3.50.1"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"name":"Key Projects of the National Natural Science Foundation of China","award":["52331012"],"award-info":[{"award-number":["52331012"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,9,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The widespread application of unmanned surface vehicles (USVs) in maritime surveillance has highlighted the need for improved ship detection models. However, hardware limitations and environmental interference impact the perception capability of USVs. In order to address these challenges, YoloS, a lightweight ship detection network, is specifically designed for USVs in complex backgrounds. Firstly, a network component Split Widely Network (SWNet) is put forward to the backbone to reduce its redundancy. SWNet leverages inter-channel interaction and the idea of group convolution to minimize redundancy of the network. Secondly, the Small Pyramid Network (SPN) is introduced as the neck network. SPN enhances the spatial information of the target regions by capturing and highlighting significant contour and texture details within target regions. SPN also utilizes these low-level features with enhanced spatial information to guide high-level features in identifying the most discriminative fine-grained details within the target regions. In order to further achieve network lightweighting, only two output heads for low-level features are retained while still preserving the capability to extract task-critical features. Extensive experiments on different datasets have verified the effectiveness of the proposed method, and it shows that YoloS can achieve 96.5$\\%$ detection accuracy and 80.1 fps on the SMD dataset with only 2.78M model parameters and 10.4G floating point operations.<\/jats:p>","DOI":"10.1093\/comjnl\/bxaf025","type":"journal-article","created":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T08:15:24Z","timestamp":1743495324000},"page":"1118-1127","source":"Crossref","is-referenced-by-count":4,"title":["A Yolo-like lightweight ship detection network for unmanned surface vehicles"],"prefix":"10.1093","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9322-3734","authenticated-orcid":false,"given":"Weina","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Information Engineering, Shanghai Maritime University , 1550 Haigang Avenue, Pudong New District, Shanghai, 201306 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4737-8371","authenticated-orcid":false,"given":"Wei","family":"Shao","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Shanghai Maritime University , 1550 Haigang Avenue, Pudong New District, Shanghai, 201306 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-0120-7169","authenticated-orcid":false,"given":"Wenhua","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Shanghai Maritime University , 1550 Haigang Avenue, Pudong New District, Shanghai, 201306 ,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,4,1]]},"reference":[{"key":"2025092201571515800_ref1","first-page":"107","article-title":"Prior-guided parallel residual bi-fusion network in usv obstacle detection","volume-title":"Proceedings of the 2023 International Conference on Consumer Electronics (ICCE)","author":"Hsu","year":"2023"},{"key":"2025092201571515800_ref2","first-page":"583","article-title":"Semantic map-based localization of usv using lidar in berthing and departing scene","volume-title":"Proceedings of the 2023 7th International Conference on Transportation Information and Safety (ICTIS)","author":"Hu","year":"2023"},{"key":"2025092201571515800_ref3","doi-asserted-by":"publisher","first-page":"11262","DOI":"10.1109\/JSEN.2022.3222575","article-title":"Research on collision avoidance algorithm of unmanned surface vehicle based on deep reinforcement learning","volume":"23","author":"Xia","year":"2023","journal-title":"IEEE Sens J"},{"key":"2025092201571515800_ref4","first-page":"6517","article-title":"Yolo9000: Better, faster, stronger","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Redmon","year":"2017"},{"key":"2025092201571515800_ref5","first-page":"779","article-title":"You only look once: Unified, real-time object detection","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Redmon","year":"2016"},{"key":"2025092201571515800_ref6","article-title":"Yolov3: An incremental improvement","author":"Redmon","year":"2018"},{"key":"2025092201571515800_ref7","article-title":"Yolov4: Optimal speed and accuracy of object detection","author":"Bochkovskiy","year":"2020"},{"key":"2025092201571515800_ref8","doi-asserted-by":"crossref","first-page":"7464","DOI":"10.1109\/CVPR52729.2023.00721","article-title":"Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","volume-title":"Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Wang","year":"2023"},{"key":"2025092201571515800_ref9","article-title":"Ultralytics yolo (version 8.0.0) [computer software]","author":"Jocher"},{"key":"2025092201571515800_ref10","unstructured":"Jocher G. \u00a02020. ultralytics\/yolov5: v3.1 - Bug fixes and performance improvements (Version v3.1) [Computer software]. Zenodo, Geneva, Switzerland. 10.5281\/zenodo.4154370."},{"key":"2025092201571515800_ref11","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1109\/JBHI.2022.3220820","article-title":"Pesa r-cnn: Perihematomal edema guided scale adaptive r-cnn for hemorrhage segmentation","volume":"27","author":"Chang","year":"2023","journal-title":"IEEE J Biomed Health Inform"},{"key":"2025092201571515800_ref12","doi-asserted-by":"publisher","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":"2017","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2025092201571515800_ref13","doi-asserted-by":"publisher","first-page":"2570","DOI":"10.1109\/TIP.2022.3148867","article-title":"Sst: Spatial and semantic transformers for multi-label image recognition","volume":"31","author":"Chen","year":"2022","journal-title":"IEEE Trans Image Process"},{"key":"2025092201571515800_ref14","doi-asserted-by":"publisher","first-page":"24327","DOI":"10.1007\/s10489-023-04865-1","article-title":"Joint learning networks of low-level and high-level features for multi-label ship recognition in complex backgrounds","volume":"53","author":"Tian","year":"2023","journal-title":"Appl Intell"},{"key":"2025092201571515800_ref15","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103768","article-title":"Efficientnet embedded with spatial attention for recognition of multi-label fundus disease from color fundus photographs","volume":"77","author":"Sun","year":"2022","journal-title":"Biomed Signal Process Control"},{"key":"2025092201571515800_ref16","doi-asserted-by":"publisher","first-page":"913","DOI":"10.3390\/rs16050913","article-title":"Yolov7osar: A lightweight high-precision ship detection model for Sar images based on the yolov7 algorithm","volume":"16","author":"Liu","year":"2024","journal-title":"Remote Sens (Basel)"},{"key":"2025092201571515800_ref17","doi-asserted-by":"publisher","first-page":"3771","DOI":"10.3390\/rs15153771","article-title":"Yolo-lite: An efficient lightweight network for Sar ship detection","volume":"15","author":"Ren","year":"2023","journal-title":"Remote Sens (Basel)"},{"key":"2025092201571515800_ref18","doi-asserted-by":"publisher","first-page":"486","DOI":"10.3390\/rs16030486","article-title":"A lightweight Sar image ship detection method based on improved convolution and yolov7","volume":"16","author":"Tang","year":"2024","journal-title":"Remote Sens (Basel)"},{"key":"2025092201571515800_ref19","doi-asserted-by":"crossref","first-page":"1577","DOI":"10.1109\/CVPR42600.2020.00165","article-title":"Ghostnet: More features from cheap operations","volume-title":"Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Han","year":"2020"},{"key":"2025092201571515800_ref20","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1109\/CVPR.2016.90","article-title":"Deep residual learning for image recognition","volume-title":"Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"He","year":"2016"},{"key":"2025092201571515800_ref21","first-page":"9628","article-title":"An intriguing failing of convolutional neural networks and the coordconv solution","volume-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems (NeurIPS)","author":"Liu","year":"2018"},{"key":"2025092201571515800_ref22","doi-asserted-by":"crossref","first-page":"9300","DOI":"10.1109\/CVPR.2019.00953","article-title":"Deformable convnets v2: More deformable, better results","volume-title":"Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Zhu","year":"2019"},{"key":"2025092201571515800_ref23","first-page":"764","article-title":"Deformable convolutional networks","volume-title":"Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV)","author":"Dai","year":"2017"},{"key":"2025092201571515800_ref24","doi-asserted-by":"publisher","first-page":"1993","DOI":"10.1109\/TITS.2016.2634580","article-title":"Video processing from electro-optical sensors for object detection and tracking in a maritime environment: A survey","volume":"18","author":"Prasad","year":"2017","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"2025092201571515800_ref25","doi-asserted-by":"publisher","DOI":"10.3389\/fnbot.2021.723336","article-title":"An image-based benchmark dataset and a novel object detector for water surface object detection","volume":"15","author":"Zhou","year":"2021","journal-title":"Front Neurorobot"},{"key":"2025092201571515800_ref26","first-page":"6848","article-title":"Shufflenet: An extremely efficient convolutional neural network for mobile devices","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Zhang","year":"2018"},{"key":"2025092201571515800_ref27","first-page":"12021","article-title":"Run, don\u2019t walk: Chasing higher flops for faster neural networks","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Chen","year":"2023"},{"key":"2025092201571515800_ref28","first-page":"2778","article-title":"Tph-yolov5: Improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW)","author":"Zhu","year":"2021"},{"key":"2025092201571515800_ref29","doi-asserted-by":"publisher","first-page":"115440","DOI":"10.1016\/j.oceaneng.2023.115440","article-title":"Deep learning based efficient ship detection from drone-captured images for maritime surveillance","volume":"285","author":"Cheng","year":"2023","journal-title":"Ocean Eng"},{"key":"2025092201571515800_ref30","doi-asserted-by":"publisher","first-page":"102606","DOI":"10.1016\/j.displa.2023.102606","article-title":"IrmultiFuseNet: Ghost hunter for infrared ship detection","volume":"81","author":"Zhou","year":"2023","journal-title":"Displays"}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/68\/9\/1118\/62828862\/bxaf025.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/68\/9\/1118\/62828862\/bxaf025.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T05:57:29Z","timestamp":1758520649000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/article\/68\/9\/1118\/8102313"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,4,1]]},"references-count":30,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,4,1]]},"published-print":{"date-parts":[[2025,9,21]]}},"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxaf025","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"value":"0010-4620","type":"print"},{"value":"1460-2067","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,9]]},"published":{"date-parts":[[2025,4,1]]}}}