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22 November 2022 Sonar pulse detection and recognition based on deep learning
Yaping Yin, Haitao Li, Shilin Sun, Zhen Liu, Yichuan Wang
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Proceedings Volume 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022); 124751K (2022) https://doi.org/10.1117/12.2659342
Event: Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 2022, Hulun Buir, China
Abstract
Sonar pulse detection and recognition is an important research direction of national marine construction. Traditional target detection and recognition methods have insufficient feature extraction capabilities and high time complexity. To solve this problem, this paper makes full use of the strong feature expression capabilities of deep neural networks. Based on the mainstream target detection networks including Faster RCNN, SSD, and YOLOv3, the sonar pulse detection and recognition method based on deep learning are deeply studied and verified on the pulse signal generated by simulation. The experimental results and analysis show that the average detection accuracy of the YOLOv3 network for sonar pulse signals can reach 92.35%, and the detection time of a single pulse signal power spectrum is only 0.018 seconds. Compared with Faster RCNN and SSD, YOLOv3 has better practicability and robustness in the field of sonar pulse detection and recognition.
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Yaping Yin, Haitao Li, Shilin Sun, Zhen Liu, and Yichuan Wang "Sonar pulse detection and recognition based on deep learning", Proc. SPIE 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 124751K (22 November 2022); https://doi.org/10.1117/12.2659342
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KEYWORDS
Target detection
Signal detection
Target recognition
Image processing
Feature extraction
Oceanography
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