Object Detection Method of Inland Vessel Based on Improved YOLO
In order to solve the problems of low accuracy of the current mainstream target detection algorithms in identifying small target ships, complex background interference such as coastline buildings and trees, and the influence of ship occlusion on ship target detection, an inland river ship detection...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/13/4/697 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849715007172902912 |
|---|---|
| author | Yaoqi Wang Jiasheng Song Yichun Wang Rongjie Wang Hongyu Chen |
| author_facet | Yaoqi Wang Jiasheng Song Yichun Wang Rongjie Wang Hongyu Chen |
| author_sort | Yaoqi Wang |
| collection | DOAJ |
| description | In order to solve the problems of low accuracy of the current mainstream target detection algorithms in identifying small target ships, complex background interference such as coastline buildings and trees, and the influence of ship occlusion on ship target detection, an inland river ship detection method based on improved YOLOv10n: CDS-YOLO is proposed under the premise of keeping the model lightweight. Firstly, the CAA attention module is introduced into the Backbone network, and the C2f_CAA module is constructed at the same time to enhance the features of the central region and improve the understanding ability of complex scenes. Then, the Conv of the Backbone network was replaced with DBB to enhance the expression ability of a single convolution and enrich the feature space. Finally, GSConv and VovGSCSP in Slim-Neck are introduced into the Neck network to optimize the network architecture, reduce part of the model complexity, and further improve the performance of the model. Experimental results show that compared with YOLOv10n, CDS-YOLO has the detection accuracy, recall rate and mAP@0.5 increased by 3.7%, 2% and 0.9% respectively, reaching 98.4%, 97.4% and 99.2% respectively, indicating that CDS-YOLO has good accuracy and robustness in the detection and classification of inshore ships. |
| format | Article |
| id | doaj-art-7cc4699a2b1f4effa78da5e2e56b7c0f |
| institution | DOAJ |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-7cc4699a2b1f4effa78da5e2e56b7c0f2025-08-20T03:13:32ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-03-0113469710.3390/jmse13040697Object Detection Method of Inland Vessel Based on Improved YOLOYaoqi Wang0Jiasheng Song1Yichun Wang2Rongjie Wang3Hongyu Chen4School of Marine Engineering, Jimei University, Xiamen 361021, ChinaSchool of Marine Engineering, Jimei University, Xiamen 361021, ChinaSchool of Marine Engineering, Jimei University, Xiamen 361021, ChinaSchool of Marine Engineering, Jimei University, Xiamen 361021, ChinaSchool of Marine Engineering, Jimei University, Xiamen 361021, ChinaIn order to solve the problems of low accuracy of the current mainstream target detection algorithms in identifying small target ships, complex background interference such as coastline buildings and trees, and the influence of ship occlusion on ship target detection, an inland river ship detection method based on improved YOLOv10n: CDS-YOLO is proposed under the premise of keeping the model lightweight. Firstly, the CAA attention module is introduced into the Backbone network, and the C2f_CAA module is constructed at the same time to enhance the features of the central region and improve the understanding ability of complex scenes. Then, the Conv of the Backbone network was replaced with DBB to enhance the expression ability of a single convolution and enrich the feature space. Finally, GSConv and VovGSCSP in Slim-Neck are introduced into the Neck network to optimize the network architecture, reduce part of the model complexity, and further improve the performance of the model. Experimental results show that compared with YOLOv10n, CDS-YOLO has the detection accuracy, recall rate and mAP@0.5 increased by 3.7%, 2% and 0.9% respectively, reaching 98.4%, 97.4% and 99.2% respectively, indicating that CDS-YOLO has good accuracy and robustness in the detection and classification of inshore ships.https://www.mdpi.com/2077-1312/13/4/697YOLOv10object detectionattention mechanismship detection |
| spellingShingle | Yaoqi Wang Jiasheng Song Yichun Wang Rongjie Wang Hongyu Chen Object Detection Method of Inland Vessel Based on Improved YOLO Journal of Marine Science and Engineering YOLOv10 object detection attention mechanism ship detection |
| title | Object Detection Method of Inland Vessel Based on Improved YOLO |
| title_full | Object Detection Method of Inland Vessel Based on Improved YOLO |
| title_fullStr | Object Detection Method of Inland Vessel Based on Improved YOLO |
| title_full_unstemmed | Object Detection Method of Inland Vessel Based on Improved YOLO |
| title_short | Object Detection Method of Inland Vessel Based on Improved YOLO |
| title_sort | object detection method of inland vessel based on improved yolo |
| topic | YOLOv10 object detection attention mechanism ship detection |
| url | https://www.mdpi.com/2077-1312/13/4/697 |
| work_keys_str_mv | AT yaoqiwang objectdetectionmethodofinlandvesselbasedonimprovedyolo AT jiashengsong objectdetectionmethodofinlandvesselbasedonimprovedyolo AT yichunwang objectdetectionmethodofinlandvesselbasedonimprovedyolo AT rongjiewang objectdetectionmethodofinlandvesselbasedonimprovedyolo AT hongyuchen objectdetectionmethodofinlandvesselbasedonimprovedyolo |