Visible Figure Part of Multi-source Maritime Ship Dataset

CUI Yaqi; ZHOU Tian; XIONG Wei; XU Saifei; LIN Chuanqi; XIA Mutao; WANG Ziling; GU Xiangqi; SUN Weiwei; LI Haoran; KONG Zhan; TANG Hao; XU Pingliang; ZHANG Jie; DAN Bo; GUO Hengguang; DONG Kai; YU Hongbo; LU Yuan; CHEN Wei; HE Shaowei

doi:10.11999/JEIT250138

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2025 >

CUI Yaqi, ZHOU Tian, XIONG Wei, XU Saifei, LIN Chuanqi, XIA Mutao, WANG Ziling, GU Xiangqi, SUN Weiwei, LI Haoran, KONG Zhan, TANG Hao, XU Pingliang, ZHANG Jie, DAN Bo, GUO Hengguang, DONG Kai, YU Hongbo, LU Yuan, CHEN Wei, HE Shaowei. Visible Figure Part of Multi-source Maritime Ship Dataset[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250138

Citation:

CUI Yaqi, ZHOU Tian, XIONG Wei, XU Saifei, LIN Chuanqi, XIA Mutao, WANG Ziling, GU Xiangqi, SUN Weiwei, LI Haoran, KONG Zhan, TANG Hao, XU Pingliang, ZHANG Jie, DAN Bo, GUO Hengguang, DONG Kai, YU Hongbo, LU Yuan, CHEN Wei, HE Shaowei. Visible Figure Part of Multi-source Maritime Ship Dataset[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250138

Citation:

CUI Yaqi, ZHOU Tian, XIONG Wei, XU Saifei, LIN Chuanqi, XIA Mutao, WANG Ziling, GU Xiangqi, SUN Weiwei, LI Haoran, KONG Zhan, TANG Hao, XU Pingliang, ZHANG Jie, DAN Bo, GUO Hengguang, DONG Kai, YU Hongbo, LU Yuan, CHEN Wei, HE Shaowei. Visible Figure Part of Multi-source Maritime Ship Dataset[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250138

PDF( 7086 KB)

Visible Figure Part of Multi-source Maritime Ship Dataset

doi: 10.11999/JEIT250138 cstr: 32379.14.JEIT250138

1.
Naval Aviation University, Yantai 264001, China
2.
Yantai Research Institute of Harbin Engineering University, Yantai 264000, China

Funds: The National Natural Science Foundation of China Joint (NSFC) Fund Project (U2433216), The National Natural Science Foundation of China General (NSFC) Program Project (62171453)

Received Date: 2025-03-10
Rev Recd Date: 2025-06-05

Available Online: 2025-06-24

Abstract

Abstract

Objective The increasing intensity of marine resource development and maritime operations has heightened the need for accurate vessel detection under complex marine conditions, which is essential for protecting maritime rights and interests. In recent years, object detection algorithms based on deep learning—such as YOLO and Faster R-CNN—have emerged as key methods for maritime target perception due to their strong feature extraction capabilities. However, their performance relies heavily on large-scale, high-quality training data. Existing general-purpose datasets, such as COCO and PASCAL VOC, offer limited vessel classes and predominantly feature static, urban, or terrestrial scenes, making them unsuitable for marine environments. Similarly, specialized datasets like SeaShips and the Singapore Marine Dataset (SMD) suffer from constraints such as limited data sources, simple scenes, small sample sizes, and incomplete coverage of marine target categories. These limitations significantly hinder further performance improvement of detection algorithms. Therefore, the development of large-scale, multimodal, and comprehensive marine-specific datasets represents a critical step toward resolving current application challenges. This effort is urgently needed to strengthen marine monitoring capabilities and ensure operational safety at sea. Methods To overcome the aforementioned challenges, a multi-sensor marine target acquisition system integrating radar, visible-light, infrared, laser, Automatic Identification System (AIS), and Global Positioning System (GPS) technologies is developed. A two-month shipborne observation campaign is conducted, yielding 200 hours of maritime monitoring and over 90 TB of multimodal raw data. To efficiently process this large volume of low-value-density data, a rapid annotation pipeline is designed, combining automated labeling with manual verification. Iterative training of intelligent annotation models, supplemented by extensive manual correction, enables the construction of the Visible Figure Part of the Multi-Source Maritime Ship Dataset (MSMS-VF). This dataset comprises 265,233 visible-light images with 1,097,268 bounding boxes across nine target categories: passenger ship, cargo vessel, speedboat, sailboat, fishing boat, buoy, floater, offshore platform, and others. Notably, 55.84% of targets are small, with pixel areas below 1,024. The dataset incorporates diverse environmental conditions including backlighting, haze, rain, and occlusion, and spans representative maritime settings such as harbor basins, open seas, and navigation channels. MSMS-VF offers a comprehensive data foundation for advancing maritime target detection, recognition, and tracking research. Results and Discussions The MSMS-VF dataset exhibits substantially greater diversity than existing datasets (Table 1, Table 2). Small targets, including buoys and floaters, occur frequently (Table 5), posing significant challenges for detection. Five object detection models—YOLO series, Real-Time Detection Transformer (RT-DETR), Faster R-CNN, Single Shot MultiBox Detector (SSD), and RetinaNet—are assessed, together with five multi-object tracking algorithms: Simple Online and Realtime Tracking (SORT), Optimal Compute for SORT (OC-SORT), DeepSORT, ByteTrack, and MotionTrack. YOLO models exhibit the most favorable trade-off between speed and accuracy. YOLOv11 achieves a mAP50 of 0.838 on the test set and a processing speed of 34.43 FPS (Table 6). However, substantial performance gaps remain for small targets; for instance, YOLOv11 yields a mAP50 of 0.549 for speedboats, markedly lower than the 0.946 obtained for large targets such as cargo vessels (Table 7). RT-DETR shows moderate performance on small objects, achieving a mAP50 of 0.532 for floaters, whereas conventional models like Faster R-CNN perform poorly, with mAP50 values below 0.1. For tracking, MotionTrack performs best under low-frame-rate conditions, achieving a MOTA of 0.606, IDF1 of 0.750, and S of 0.681 using a Gaussian distance cascade-matching strategy (Table 8, Fig. 14). Conclusions This study presents the MSMS-VF dataset, which offers essential data support for maritime perception research through its integration of multi-source inputs, diverse environmental scenarios, and a high proportion of small targets. Experimental validation confirms the dataset’s utility in training and evaluating state-of-the-art algorithms, while also revealing persistent challenges in detecting and tracking small objects under dynamic maritime conditions. Nevertheless, the dataset has limitations. The current data are predominantly sourced from waters near Yantai, leading to imbalanced ship-type representation and the absence of certain vessel categories. Future efforts will focus on expanding data acquisition to additional maritime regions, broadening the scope of multi-source data collection, and incrementally releasing extended components of the dataset to support ongoing research.
- Maritime ship target perception,
- Large-scale maritime ship dataset,
- Small target detection,
- Deep learning

FullText(HTML)

References(33)

References

[1]	PERERA L P, OLIVEIRA P, and SOARES C G. Maritime traffic monitoring based on vessel detection, tracking, state estimation, and trajectory prediction[J]. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(3): 1188–1200. doi: 10.1109/TITS.2012.2187282.
[2]	LIU Yand, AN Bailin, CHEN Shaohua, et al. Multi‐target detection and tracking of shallow marine organisms based on improved YOLO v5 and DeepSORT[J]. IET Image Processing, 2024, 18(9): 2273–2290. doi: 10.1049/ipr2.13090.
[3]	LIU Zhixiang, ZHANG Youmin, YU Xiang, et al. Unmanned surface vehicles: An overview of developments and challenges[J]. Annual Reviews in Control, 2016, 41: 71–93. doi: 10.1016/j.arcontrol.2016.04.018.
[4]	尹宏鹏, 陈波, 柴毅, 等. 基于视觉的目标检测与跟踪综述[J]. 自动化学报, 2016, 42(10): 1466–1489. doi: 10.16383/j.aas.2016.c150823. YIN Hongpeng, CHEN Bo, CHAI Yi, et al. Vision-based object detection and tracking: A review[J]. Acta Automatica Sinica, 2016, 42(10): 1466–1489. doi: 10.16383/j.aas.2016.c150823.
[5]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: Unified, real-time object detection[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA, 2016: 779–788. doi: 10.1109/CVPR.2016.91.
[6]	ZHAO Yian, LV Wenyu, XU Shangliang, et al. DETRs beat YOLOs on real-time object detection[C]. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, USA, 2024: 16965–16974. doi: 10.1109/CVPR52733.2024.01605.
[7]	REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137–1149. doi: 10.1109/TPAMI.2016.2577031.
[8]	JIANG Zhikai, SU Li, and SUN Yixin. YOLOv7-ship: A lightweight algorithm for ship object detection in complex marine environments[J]. Journal of Marine Science and Engineering, 2024, 12(1): 190. doi: 10.3390/jmse12010190.
[9]	FAN Xiyu, HU Zhuhua, ZHAO Yaochi, et al. A small-ship object detection method for satellite remote sensing data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2024, 17: 11886–11898. doi: 10.1109/JSTARS.2024.3419786.
[10]	YANG Defu, SOLIHIN M I, ARDIYANTO I, et al. Author correction: A streamlined approach for intelligent ship object detection using EL-YOLO algorithm[J]. Scientific Reports, 2024, 14(1): 19408. doi: 10.1038/s41598-024-70017-1.
[11]	GUO Yiran, SHEN Qiang, AI Danni, et al. Sea-IoUTracker: A more stable and reliable maritime target tracking scheme for unmanned vessel platforms[J]. Ocean Engineering, 2024, 299: 117243. doi: 10.1016/j.oceaneng.2024.117243.
[12]	LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: Common objects in context[C]. The 13th European Conference on Computer Vision, Zurich, Switzerland, 2014: 740–755. doi: 10.1007/978-3-319-10602-1_48.
[13]	EVERINGHAM M, VAN GOOL L, WILLIAMS C K I, et al. The pascal Visual Object Classes (VOC) challenge[J]. International Journal of Computer Vision, 2010, 88(2): 303–338. doi: 10.1007/s11263-009-0275-4.
[14]	DENG Jia, DONG Wei, SOCHER R, et al. ImageNet: A large-scale hierarchical image database[C]. 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, 2009: 248–255. doi: 10.1109/CVPR.2009.5206848.
[15]	KUZNETSOVA A, ROM H, ALLDRIN N, et al. The open images dataset V4: Unified image classification, object detection, and visual relationship detection at scale[J]. International Journal of Computer Vision, 2020, 128(7): 1956–1981. doi: 10.1007/S11263-020-01316-Z.
[16]	SHAO Zhenfeng, WU Wenjing, WANG Zhongyuan, et al. SeaShips: A large-scale precisely-annotated dataset for ship detection[J]. IEEE Transactions on Multimedia, 2018, 20(10): 2593–2604. doi: 10.1109/TMM.2018.2865686.
[17]	PRASAD D K, RAJAN D, RACHMAWATI L, et al. Video processing from electro-optical sensors for object detection and tracking in a maritime environment: A survey[J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(8): 1993–2016. doi: 10.1109/TITS.2016.2634580.
[18]	IANCU B, SOLOVIEV V, ZELIOLI L, et al. ABOships—an inshore and offshore maritime vessel detection dataset with precise annotations[J]. Remote Sensing, 2021, 13(5): 988. doi: 10.3390/rs13050988.
[19]	HE Boyong, LI Xianjiang, HUANG Bo, et al. UnityShip: A large-scale synthetic dataset for ship recognition in aerial images[J]. Remote Sensing, 2021, 13(24): 4999. doi: 10.3390/rs13244999.
[20]	ZHENG Yitong and ZHANG Shun. Mcships: A large-scale ship dataset for detection and fine-grained categorization in the wild[C]. 2020 IEEE International Conference on Multimedia and Expo (ICME), London, UK, 2020: 1–6. doi: 10.1109/ICME46284.2020.9102907.
[21]	NANDA A, CHO S W, LEE H, et al. KOLOMVERSE: Korea open large-scale image dataset for object detection in the maritime universe[J]. IEEE Transactions on Intelligent Transportation Systems, 2024, 25(12): 20832–20840. doi: 10.1109/TITS.2024.3449122.
[22]	何友, 周伟. 海上信息感知大数据技术[J]. 指挥信息系统与技术, 2018, 9(2): 1–7. doi: 10.15908/j.cnki.cist.2018.02.001. HE You and ZHOU Wei. Big data technology for maritime information sensing[J]. Command Information System and Technology, 2018, 9(2): 1–7. doi: 10.15908/j.cnki.cist.2018.02.001.
[23]	CHENG Gong, YUAN Xiang, and YAO Xiwen, et al. Towards large-scale small object detection: Survey and benchmarks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(11): 13467–13488. doi: 10.1109/TPAMI.2023.3290594.
[24]	LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: Single shot MultiBox detector[C]. The 14th European Conference on Computer Vision, Amsterdam, The Netherlands, 2016: 21–37. doi: 10.1007/978-3-319-46448-0_2.
[25]	LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 318–327. doi: 10.1109/TPAMI.2018.2858826.
[26]	JOCHER G, CHAURASIA A, STOKEN A, et al. Ultralytics/yolov5: V6.1-TensorRT, TensorFlow edge TPU and OpenVINO export and inference[J]. Zenodo, 2022. doi: 10.5281/zenodo.6222936.
[27]	AKYON F C. Yolov8.3. 62[EB/OL]. https://github.com/ultralytics/ultralytics/releases/tag/v8.3.62, 2024.
[28]	KHANAM R and HUSSAIN M. YOLOv11: An overview of the key architectural enhancements[EB/OL]. https://arxiv.org/abs/2410.17725, 2024.
[29]	BEWLEY A, GE Zongyuan, OTT L, et al. Simple online and realtime tracking[C]. 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, USA, 2016: 3464–3468. doi: 10.1109/ICIP.2016.7533003.
[30]	CAO Jinkun, PANG Jiangmiao, WENG Xinshuo, et al. Observation-centric SORT: Rethinking SORT for robust multi-object tracking[C]. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, Canada, 2023: 9686–9696. doi: 10.1109/CVPR52729.2023.00934.
[31]	WOJKE N, BEWLEY A, and PAULUS D. Simple online and realtime tracking with a deep association metric[C]. 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, 2017: 3645–3649. doi: 10.1109/ICIP.2017.8296962.
[32]	ZHANG Yifu, SUN Peize, JIANG Yi, et al. ByteTrack: Multi-object tracking by associating every detection box[C]. The 17th European Conference on Computer Vision, Tel Aviv, Israel, 2022: 1–21. doi: 10.1007/978-3-031-20047-2_1.
[33]	肖刚, 梁振起, 曾柳, 等. 基于高斯距离匹配的海面多目标跟踪方法及系统[P]. 中国, 202211457200.0, 2023. XIAO Gang, LIANG Zhenqi, ZENG Liu, et al. Sea surface multi-target tracking method and system based on Gaussian distance matching[P]. China, 202211457200.0, 2023.