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. D2MFNet: a Dual-Domain Multi-Frequency Network for Side-Scan Sonar Image Detection. Preprints2023, 2023092024. https://doi.org/10.20944/preprints202309.2024.v1
Wang, W.; Zhang, Y.; Li, H.; Gong, X.; Liu, L.; Kang, Y. D2MFNet: a Dual-Domain Multi-Frequency Network for Side-Scan Sonar Image Detection. Preprints 2023, 2023092024. https://doi.org/10.20944/preprints202309.2024.v1
Wang, W.; Zhang, Y.; Li, H.; Gong, X.; Liu, L.; Kang, Y. D2MFNet: a Dual-Domain Multi-Frequency Network for Side-Scan Sonar Image Detection. Preprints2023, 2023092024. https://doi.org/10.20944/preprints202309.2024.v1
APA Style
Wang, W., Zhang, Y., Li, H., Gong, X., Liu, L., & Kang, Y. (2023). D2MFNet: a Dual-Domain Multi-Frequency Network for Side-Scan Sonar Image Detection. Preprints. https://doi.org/10.20944/preprints202309.2024.v1
Chicago/Turabian Style
Wang, W., Lei Liu and Yixin Kang. 2023 "D2MFNet: a Dual-Domain Multi-Frequency Network for Side-Scan Sonar Image Detection" Preprints. https://doi.org/10.20944/preprints202309.2024.v1
Abstract
The utilization of side-scan sonar (SSS) technology plays a pivotal role in ensuring the safety of underwater environments and facilitating the discovery of aquatic resources during daily production activities. Within this field, the use of SSS images for seabed target detection has gained widespread recognition. 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.
To maximize the information extracted from SSS images across various frequency ranges, we introduce a novel method for calculating the information flow of target ranges and propose a Multi-Frequency Combined Attention Mechanism (MFCAM). This mechanism amplifies the relevance of dual-domain features across different channels. 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. 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.
