A Sonar Image Target Detection Method with Low False Alarm Rate Based on Self-Trained YOLO11 Model

HAN Jingqi; NAN Mingxing; ZHANG Peng; CHEN Jiajie; HU Zhengliang

doi:10.11993/j.issn.2096-3920.2024-0165

Volume 33 Issue 2

May 2025

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Article Navigation > Journal of Unmanned Undersea Systems > 2025 > 33(2): 238-248

HAN Jingqi, NAN Mingxing, ZHANG Peng, CHEN Jiajie, HU Zhengliang. A Sonar Image Target Detection Method with Low False Alarm Rate Based on Self-Trained YOLO11 Model[J]. Journal of Unmanned Undersea Systems, 2025, 33(2): 238-248. doi: 10.11993/j.issn.2096-3920.2024-0165

Citation:

HAN Jingqi, NAN Mingxing, ZHANG Peng, CHEN Jiajie, HU Zhengliang. A Sonar Image Target Detection Method with Low False Alarm Rate Based on Self-Trained YOLO11 Model[J]. Journal of Unmanned Undersea Systems, 2025, 33(2): 238-248. doi: 10.11993/j.issn.2096-3920.2024-0165

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A Sonar Image Target Detection Method with Low False Alarm Rate Based on Self-Trained YOLO11 Model

doi: 10.11993/j.issn.2096-3920.2024-0165

College of Metearology and Oceanography, National University of Defense Technology, Changsha 410073, China

Received Date: 2024-12-12
Accepted Date: 2025-03-10
Rev Recd Date: 2025-03-04

Available Online: 2025-03-19

Abstract

Abstract

Autonomous detection of sonar image targets is a key technology for unmanned undersea systems, but it faces the challenge of high false alarm rates in practical applications, which limits the quality and efficiency of mission execution by unmanned underwater systems. In this paper, an underwater target detection method based on the YOLO11 model was designed, and a false alarm rate detection method by self-training a deep learning detector on sonar images was proposed to reduce the false alarm rate. This method automatically generated proxy classification tasks based on the sonar image target detection dataset and improved the deep learning detector’s learning of target and background features through pre-training, enhancing the detector’s ability to distinguish between targets and backgrounds and thereby reducing the false alarm rate. Experimental results demonstrate that when the detector’s confidence is set to the value corresponding to the maximum F₁-score, the YOLO11 detector trained using the proposed method can reduce the false alarm rate by 11.60% compared to traditional transfer learning methods while achieving a higher recall rate. This method improves the generalization of the deep learning detector without using external datasets, providing an efficient self-training approach for underwater target detection scenarios with small sample sizes.
- underwater target detection,
- false alarm rate,
- sonar image processing,
- deep learning

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References(26)

References

[1]	LAW H, DENG J. CornerNet: Detecting objects as paired keypoints[J]. International Journal of Computer Vision. 2020, 128(3): 642-656.
[2]	BARHOUMI C, BENAYED Y. Real-time speech emotion recognition using deep learning and data augmentation[J]. Artificial Intelligence Review. 2025, 58: 49.
[3]	SHAO Y, ZHANG D, CHU H, et al. A review of YOLO object detection based on deep learning[J]. Journal of Electronics and Information Technology, 2022, 44(10): 3697-3708.
[4]	ZHANG P, TANG J, ZHONG H, et al. Self-trained target detection of radar and sonar images using automatic deep learning[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60(1): 1-14.
[5]	HUO G, WU Z, LI J. Underwater object classification in sidescan sonar images using deep transfer learning and semisynthetic training data[J]. IEEE Access, 2020, 8: 47407-47418. doi: 10.1109/ACCESS.2020.2978880
[6]	WILLIAMS D. P. Underwater target classification in synthetic aperture sonar imagery using deep convolutional neural networks[C]//2016 23rd International Conference on Pattern Recognition(ICPR). Cancun, Mexico: ICPR, 2016: 2497-2502.
[7]	WANG X, JIAO J, YIN J, et al. Underwater sonar image classification using adaptive weights convolutional neural network[J]. Applied Acoustics, 2019, 146: 145-154. doi: 10.1016/j.apacoust.2018.11.003
[8]	JOCHER G, QIU J. Ultralytics YOLO11[CP/OL]. (2024) [2025-01-30]. https://github.com/ultralytics/ultralytics .
[9]	LI Z, CHEN D, YIP T, et al. Sparsity regularization-based real-time target recognition for side scan sonar with embedded GPU[J]. Journal of Marine Science and Engineering, 2023, 11(3): 487.
[10]	CHEN Z, XIE G, DENG X, et al. DA-YOLOv7: A deep learning-driven high-performance underwater sonar image target recognition model[J]. Journal of Marine Science and Engineering, 2024, 12(9): 1606. doi: 10.3390/jmse12091606
[11]	ZHENG K, LIANG H, ZHAO H, et al. Application and analysis of the MFF-YOLOv7 model in underwater sonar image target detection[J]. Journal of Marine Science and Engineering, 2024, 12(12): 2326.
[12]	KARIMANZIRA D, RENKEWITZ H, SHEA D, et al. Object detection in sonar images[J]. Electronics, 2020, 9(7): 1180.
[13]	王闰成. 侧扫声呐图像变形现象与实例分析[J]. 海洋测绘, 2002(5): 42-45. doi: 10.3969/j.issn.1671-3044.2002.05.011 WANG R C. Analysis of distortion phenomena and case studies in side-scan sonar images[J]. Hydrographic Surveying and Charting, 2002(5): 42-45. doi: 10.3969/j.issn.1671-3044.2002.05.011
[14]	HOŻYŃ S. A review of underwater mine detection and classification in sonar imagery[J]. Electronics, 2021, 10(23): 2943.
[15]	PALOMERAS N, FURFARO T, WILLIAMS D P, et al. Automatic target recognition for mine countermeasure missions using forward-looking sonar data[J]. IEEE Journal of Oceanic Engineering, 2022, 47(1):141-161.
[16]	SONG Y, HE B, LIU P. Real-time object detection for AUVs using self-cascaded convolutional neural networks[J]. IEEE Journal of Oceanic Engineering, 2021, 46(1): 56-67. doi: 10.1109/JOE.2019.2950974
[17]	MA Q, JIANG L, YU W, et al. Training with noise adversarial network: A generalization method for object detection on sonar image[C]//IEEE Winter Conference on Applications of Computer Vision. Snowmass Village, CO, USA, 2020: 718-727.
[18]	HUANG C, ZHAO J, ZHANG H, et al. Seg2Sonar: A full-class sample synthesis method applied to underwater sonar image target detection, recognition, and segmentation tasks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 1-19.
[19]	YU Y, ZHAO J, GONG Q, et al. Real-time underwater maritime object detection in side-scan sonar images based on transformer-YOLOv5[J]. Remote Sensing, 2021, 13(18): 3555.
[20]	DENG J, DONG W, SOCHER R, et al. ImageNet: A large-scale hierarchical image database[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA: IEEE, 2009: 248-255.
[21]	FERREIRA F, MACHADO D, FERRI G, et al. Underwater optical and acoustic imaging: A time for fusion? A brief overview of the state-of-the-art[C]//OCEANS 2016 MTS/IEEE Monterey. Monterey, California, USA: IEEE, 2016:1-6.
[22]	REED S, PETILLOT Y, BELL J. Automated approach to classification of mine-like objects in sidescan sonar using highlight and shadow information[J]. Radar, Sonar and Navigation, 2004, 151: 48-56.
[23]	JOCHER G, CHAURASIA A, QIU J. Ultralytics YOLOv8[CP/OL]. [2025-01-30]. https://github.com/ultralytics/ultralytics.
[24]	JOCHER G. Ultralytics YOLOv5[CP/OL]. [2025-01-30]. https://github.com/ultralytics/yolov5.
[25]	WANG A, CHEN H, LIU L, et al. YOLOv10: real-time end-to-end object detection[EB/OL]. (2024-10-30)[2025-01-30]. https://arxiv.org/abs/2405.14458.
[26]	REN S, HE K, GIRSHICK R, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(6): 1137-1149.