Version 1
: Received: 27 September 2023 / Approved: 28 September 2023 / Online: 29 September 2023 (03:40:12 CEST)
Version 2
: Received: 27 October 2023 / Approved: 1 November 2023 / Online: 2 November 2023 (07:13:35 CET)
How to cite:
Wang, W.; Zhang, Y.; Li, H.; Gong, X.; Liu, L.; Kang, Y. A Novel Target Detection Method with Dual-Domain Multi-Frequency Feature in Side-Scan Sonar Images. Preprints2023, 2023092024. https://doi.org/10.20944/preprints202309.2024.v2
Wang, W.; Zhang, Y.; Li, H.; Gong, X.; Liu, L.; Kang, Y. A Novel Target Detection Method with Dual-Domain Multi-Frequency Feature in Side-Scan Sonar Images. Preprints 2023, 2023092024. https://doi.org/10.20944/preprints202309.2024.v2
Wang, W.; Zhang, Y.; Li, H.; Gong, X.; Liu, L.; Kang, Y. A Novel Target Detection Method with Dual-Domain Multi-Frequency Feature in Side-Scan Sonar Images. Preprints2023, 2023092024. https://doi.org/10.20944/preprints202309.2024.v2
APA Style
Wang, W., Zhang, Y., Li, H., Gong, X., Liu, L., & Kang, Y. (2023). A Novel Target Detection Method with Dual-Domain Multi-Frequency Feature in Side-Scan Sonar Images. Preprints. https://doi.org/10.20944/preprints202309.2024.v2
Chicago/Turabian Style
Wang, W., Lei Liu and Yixin Kang. 2023 "A Novel Target Detection Method with Dual-Domain Multi-Frequency Feature in Side-Scan Sonar Images" Preprints. https://doi.org/10.20944/preprints202309.2024.v2
Abstract
Side-scan sonar (SSS) detection is a key method in applications such as underwater environmental security and subsea resource development. The use of acoustic images for seabed target detection has gradually become a mainstream underwater detection method. However, many existing detection approaches primarily concentrate on tracking the evolution path of optical image object detection tasks, resulting in complex structures and limited versatility. To tackle this issue, we introduce a pioneering Dual-Domain Multi-Frequency Network (D2MFNet) meticulously crafted to harness the distinct characteristics of SSS image detection. In D2MFNet, aiming at the underwater detection requirements of small scenes, we introduce a novel method for optimize and improve the detection sensitivity of different frequency ranges and propose a Multi-Frequency Combined Attention Mechanism (MFCAM). This mechanism amplifies the relevance of dual-domain features across different channels and space. Moreover, recognizing that SSS images can provide richer insights after frequency domain conversion, we introduce a Dual-Domain Feature Pyramid Network (D2FPN). By incorporating frequency domain information representation, D2FPN significantly augments the depth and breadth of feature information in underwater small datasets. Our methods are seamlessly designed for integration into existing networks, offering plug-and-play functionality with substantial performance enhancements. We have conducted extensive experiments to validate the efficacy of our proposed techniques, and the results showcase their state-of-the-art performance. MFCAM improves the mAP by 16.9% in the KLSG dataset and 15.5% in the SCTD dataset. The mAP of D2FPN was improved by 8.4% in the KLSG dataset and by 9.8% in the SCTD dataset. We will make our code and models publicly available at https://dagshub.com/estrellaww00/D2MFNet.
Keywords
target detection; side-scan sonar images; seabed object; frequency domain
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Commenter: Wen Wang
Commenter's Conflict of Interests: Author