Side-Scan Sonar Small Objects Detection Based on Improved YOLOv11
Underwater object detection using side-scan sonar (SSS) remains a significant challenge in marine exploration, especially for small objects. Conventional methods for small object detection face various obstacles, such as difficulties in feature extraction and the considerable impact of noise on dete...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/1/162 |
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| author | Chang Zou Siquan Yu Yankai Yu Haitao Gu Xinlin Xu |
| author_facet | Chang Zou Siquan Yu Yankai Yu Haitao Gu Xinlin Xu |
| author_sort | Chang Zou |
| collection | DOAJ |
| description | Underwater object detection using side-scan sonar (SSS) remains a significant challenge in marine exploration, especially for small objects. Conventional methods for small object detection face various obstacles, such as difficulties in feature extraction and the considerable impact of noise on detection accuracy. To address these issues, this study proposes an improved YOLOv11 network named YOLOv11-SDC. Specifically, a new Sparse Feature (SF) module is proposed, replacing the Spatial Pyramid Pooling Fast (SPPF) module from the original YOLOv11 architecture to enhance object feature selection. Furthermore, the proposed YOLOv11-SDC integrates a Dilated Reparam Block (DRB) with a C3k2 module to broaden the model’s receptive field. A Content-Guided Attention Fusion (CGAF) module is also incorporated prior to the detection module to assign appropriate weights to various feature maps, thereby emphasizing the relevant object information. Experimental results clearly demonstrate the superiority of YOLOv11-SDC over several iterations of YOLO versions in detection performance. The proposed method was validated through extensive real-world experiments, yielding a precision of 0.934, recall of 0.698, mAP@0.5 of 0.825, and mAP@0.5:0.95 of 0.598. In conclusion, the improved YOLOv11-SDC offers a promising solution for detecting small objects in SSS images, showing substantial potential for marine applications. |
| format | Article |
| id | doaj-art-8f3c3cebbbaa4215ad2c7413db5d12a1 |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-8f3c3cebbbaa4215ad2c7413db5d12a12025-01-24T13:37:05ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-01-0113116210.3390/jmse13010162Side-Scan Sonar Small Objects Detection Based on Improved YOLOv11Chang Zou0Siquan Yu1Yankai Yu2Haitao Gu3Xinlin Xu4State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaState Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, ChinaUniversity of Chinese Academy of Sciences, Beijing 100049, ChinaUnderwater object detection using side-scan sonar (SSS) remains a significant challenge in marine exploration, especially for small objects. Conventional methods for small object detection face various obstacles, such as difficulties in feature extraction and the considerable impact of noise on detection accuracy. To address these issues, this study proposes an improved YOLOv11 network named YOLOv11-SDC. Specifically, a new Sparse Feature (SF) module is proposed, replacing the Spatial Pyramid Pooling Fast (SPPF) module from the original YOLOv11 architecture to enhance object feature selection. Furthermore, the proposed YOLOv11-SDC integrates a Dilated Reparam Block (DRB) with a C3k2 module to broaden the model’s receptive field. A Content-Guided Attention Fusion (CGAF) module is also incorporated prior to the detection module to assign appropriate weights to various feature maps, thereby emphasizing the relevant object information. Experimental results clearly demonstrate the superiority of YOLOv11-SDC over several iterations of YOLO versions in detection performance. The proposed method was validated through extensive real-world experiments, yielding a precision of 0.934, recall of 0.698, mAP@0.5 of 0.825, and mAP@0.5:0.95 of 0.598. In conclusion, the improved YOLOv11-SDC offers a promising solution for detecting small objects in SSS images, showing substantial potential for marine applications.https://www.mdpi.com/2077-1312/13/1/162underwater small object detectioncomputer visionYOLOv11 |
| spellingShingle | Chang Zou Siquan Yu Yankai Yu Haitao Gu Xinlin Xu Side-Scan Sonar Small Objects Detection Based on Improved YOLOv11 Journal of Marine Science and Engineering underwater small object detection computer vision YOLOv11 |
| title | Side-Scan Sonar Small Objects Detection Based on Improved YOLOv11 |
| title_full | Side-Scan Sonar Small Objects Detection Based on Improved YOLOv11 |
| title_fullStr | Side-Scan Sonar Small Objects Detection Based on Improved YOLOv11 |
| title_full_unstemmed | Side-Scan Sonar Small Objects Detection Based on Improved YOLOv11 |
| title_short | Side-Scan Sonar Small Objects Detection Based on Improved YOLOv11 |
| title_sort | side scan sonar small objects detection based on improved yolov11 |
| topic | underwater small object detection computer vision YOLOv11 |
| url | https://www.mdpi.com/2077-1312/13/1/162 |
| work_keys_str_mv | AT changzou sidescansonarsmallobjectsdetectionbasedonimprovedyolov11 AT siquanyu sidescansonarsmallobjectsdetectionbasedonimprovedyolov11 AT yankaiyu sidescansonarsmallobjectsdetectionbasedonimprovedyolov11 AT haitaogu sidescansonarsmallobjectsdetectionbasedonimprovedyolov11 AT xinlinxu sidescansonarsmallobjectsdetectionbasedonimprovedyolov11 |