A High-Accuracy Underwater Object Detection Algorithm for Synthetic Aperture Sonar Images
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| Publicat a: | Remote Sensing vol. 17, no. 13 (2025), p. 2112-2134 |
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| Altres autors: | , , , , |
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MDPI AG
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| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3229156718 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2072-4292 | ||
| 024 | 7 | |a 10.3390/rs17132112 |2 doi | |
| 035 | |a 3229156718 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231556 |2 nlm | ||
| 100 | 1 | |a Su Jiahui |u School of Ocean Information Engineering, Jimei University, Xiamen 361021, China | |
| 245 | 1 | |a A High-Accuracy Underwater Object Detection Algorithm for Synthetic Aperture Sonar Images | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Underwater object detection with Synthetic Aperture Sonar (SAS) images faces many problems, including low contrast, blurred edges, high-frequency noise, and missed small objects. To improve these problems, this paper proposes a high-accuracy underwater object detection algorithm for SAS images, named the HAUOD algorithm. First, considering SAS image characteristics, a sonar preprocessing module is designed to enhance the signal-to-noise ratio of object features. This module incorporates three-stage processing for image quality optimization, and the three stages include collaborative adaptive Contrast Limited Adaptive Histogram Equalization (CLAHE) enhancement, non-local mean denoising, and frequency-domain band-pass filtering. Subsequently, a novel C2fD module is introduced to replace the original C2f module to strengthen perception capabilities for low-contrast objects and edge-blurred regions. The proposed C2fD module integrates spatial differential feature extraction, dynamic feature fusion, and Enhanced Efficient Channel Attention (Enhanced ECA). Furthermore, an underwater multi-scale contextual attention mechanism, named UWA, is introduced to enhance the model’s discriminative ability for multi-scale objects and complex backgrounds. The proposed UWA module combines noise suppression, hierarchical dilated convolution groups, and dual-dimensional attention collaboration. Experiments on the Sonar Common object Detection dataset (SCTD) demonstrate that the proposed HAUOD algorithm achieves superior performance in small object detection accuracy and multi-scenario robustness, attaining a detection accuracy of <inline-formula>95.1%</inline-formula>, which is <inline-formula>8.3%</inline-formula> higher than the baseline model (YOLOv8n). Compared with YOLOv8s, the proposed HAUOD algorithm can achieve <inline-formula>6.2%</inline-formula> higher accuracy with only <inline-formula>50.4%</inline-formula> model size, and reduce the computational complexity by half. Moreover, the HAUOD method exhibits significant advantages in balancing computational efficiency and accuracy compared to mainstream detection models. | |
| 653 | |a Feature extraction | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Algorithms | ||
| 653 | |a Sound waves | ||
| 653 | |a Signal processing | ||
| 653 | |a Sonar | ||
| 653 | |a Drownings | ||
| 653 | |a Computer applications | ||
| 653 | |a Modules | ||
| 653 | |a Localization | ||
| 653 | |a Efficiency | ||
| 653 | |a Noise reduction | ||
| 653 | |a Design | ||
| 653 | |a Image quality | ||
| 653 | |a Complexity | ||
| 653 | |a Object recognition | ||
| 653 | |a Acoustics | ||
| 653 | |a Underwater | ||
| 653 | |a Bandpass filters | ||
| 653 | |a Synthetic apertures | ||
| 653 | |a Signal to noise ratio | ||
| 700 | 1 | |a Xu Deyin |u School of Ocean Information Engineering, Jimei University, Xiamen 361021, China | |
| 700 | 1 | |a Qiu, Lu |u School of Ocean Information Engineering, Jimei University, Xiamen 361021, China | |
| 700 | 1 | |a Xu, Zhiping |u School of Ocean Information Engineering, Jimei University, Xiamen 361021, China | |
| 700 | 1 | |a Lin, Lixiong |u School of Ocean Information Engineering, Jimei University, Xiamen 361021, China | |
| 700 | 1 | |a Zheng Jiachun |u School of Ocean Information Engineering, Jimei University, Xiamen 361021, China | |
| 773 | 0 | |t Remote Sensing |g vol. 17, no. 13 (2025), p. 2112-2134 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3229156718/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3229156718/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3229156718/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |