LW-YOLO11: A Lightweight Arbitrary-Oriented Ship Detection Method Based on Improved YOLO11
Arbitrary-oriented ship detection has become challenging due to problems of high resolution, poor imaging clarity, and large size differences between targets in remote sensing images. Most of the existing ship detection methods are difficult to use simultaneously to meet the requirements of high acc...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/1/65 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841548968622817280 |
|---|---|
| author | Jianwei Huang Kangbo Wang Yue Hou Jiahe Wang |
| author_facet | Jianwei Huang Kangbo Wang Yue Hou Jiahe Wang |
| author_sort | Jianwei Huang |
| collection | DOAJ |
| description | Arbitrary-oriented ship detection has become challenging due to problems of high resolution, poor imaging clarity, and large size differences between targets in remote sensing images. Most of the existing ship detection methods are difficult to use simultaneously to meet the requirements of high accuracy and speed. Therefore, we designed a lightweight and efficient multi-scale feature dilated neck module in the YOLO11 network to achieve the high-precision detection of arbitrary-oriented ships in remote sensing images. Firstly, multi-scale dilated attention is utilized to effectively capture the multi-scale semantic details of ships in remote sensing images. Secondly, the interaction between the spatial information of remote sensing images and the semantic information of low-resolution features of ships is realized by using the cross-stage partial stage. Finally, the GSConv module is introduced to minimize the loss of semantic information on ship features during transmission. The experimental results show that the proposed method has the advantages of light structure and high accuracy, and the ship detection performance is better than the state-of-the-art detection methods. Compared with YOLO11n, it improves 3.1% of mAP@0.5 and 3.3% of mAP@0.5:0.95 on the HRSC2016 dataset and 1.9% of mAP@0.5 and 1.3% of mAP@0.5:0.95 on the MMShip dataset. |
| format | Article |
| id | doaj-art-126b2c13a160450998b342c0a1b18a07 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-126b2c13a160450998b342c0a1b18a072025-01-10T13:20:45ZengMDPI AGSensors1424-82202024-12-012516510.3390/s25010065LW-YOLO11: A Lightweight Arbitrary-Oriented Ship Detection Method Based on Improved YOLO11Jianwei Huang0Kangbo Wang1Yue Hou2Jiahe Wang3College of Power Engineering, Naval University of Engineering, Wuhan 430033, ChinaSimulation Training Center, Naval University of Engineering, Wuhan 430033, ChinaCollege of Power Engineering, Naval University of Engineering, Wuhan 430033, ChinaCollege of Power Engineering, Naval University of Engineering, Wuhan 430033, ChinaArbitrary-oriented ship detection has become challenging due to problems of high resolution, poor imaging clarity, and large size differences between targets in remote sensing images. Most of the existing ship detection methods are difficult to use simultaneously to meet the requirements of high accuracy and speed. Therefore, we designed a lightweight and efficient multi-scale feature dilated neck module in the YOLO11 network to achieve the high-precision detection of arbitrary-oriented ships in remote sensing images. Firstly, multi-scale dilated attention is utilized to effectively capture the multi-scale semantic details of ships in remote sensing images. Secondly, the interaction between the spatial information of remote sensing images and the semantic information of low-resolution features of ships is realized by using the cross-stage partial stage. Finally, the GSConv module is introduced to minimize the loss of semantic information on ship features during transmission. The experimental results show that the proposed method has the advantages of light structure and high accuracy, and the ship detection performance is better than the state-of-the-art detection methods. Compared with YOLO11n, it improves 3.1% of mAP@0.5 and 3.3% of mAP@0.5:0.95 on the HRSC2016 dataset and 1.9% of mAP@0.5 and 1.3% of mAP@0.5:0.95 on the MMShip dataset.https://www.mdpi.com/1424-8220/25/1/65arbitrary-oriented ship detectionlightweight networksimproved YOLO11GSConv modulemulti-scale dilated attentioncross-stage partial stage |
| spellingShingle | Jianwei Huang Kangbo Wang Yue Hou Jiahe Wang LW-YOLO11: A Lightweight Arbitrary-Oriented Ship Detection Method Based on Improved YOLO11 Sensors arbitrary-oriented ship detection lightweight networks improved YOLO11 GSConv module multi-scale dilated attention cross-stage partial stage |
| title | LW-YOLO11: A Lightweight Arbitrary-Oriented Ship Detection Method Based on Improved YOLO11 |
| title_full | LW-YOLO11: A Lightweight Arbitrary-Oriented Ship Detection Method Based on Improved YOLO11 |
| title_fullStr | LW-YOLO11: A Lightweight Arbitrary-Oriented Ship Detection Method Based on Improved YOLO11 |
| title_full_unstemmed | LW-YOLO11: A Lightweight Arbitrary-Oriented Ship Detection Method Based on Improved YOLO11 |
| title_short | LW-YOLO11: A Lightweight Arbitrary-Oriented Ship Detection Method Based on Improved YOLO11 |
| title_sort | lw yolo11 a lightweight arbitrary oriented ship detection method based on improved yolo11 |
| topic | arbitrary-oriented ship detection lightweight networks improved YOLO11 GSConv module multi-scale dilated attention cross-stage partial stage |
| url | https://www.mdpi.com/1424-8220/25/1/65 |
| work_keys_str_mv | AT jianweihuang lwyolo11alightweightarbitraryorientedshipdetectionmethodbasedonimprovedyolo11 AT kangbowang lwyolo11alightweightarbitraryorientedshipdetectionmethodbasedonimprovedyolo11 AT yuehou lwyolo11alightweightarbitraryorientedshipdetectionmethodbasedonimprovedyolo11 AT jiahewang lwyolo11alightweightarbitraryorientedshipdetectionmethodbasedonimprovedyolo11 |