SEANet: Semantic Enhancement and Amplification for Underwater Object Detection in Complex Visual Scenarios
Detecting underwater objects is a complex task due to the inherent challenges of low contrast and intricate backgrounds. The wide range of object scales further complicates detection accuracy. To address these issues, we propose a Semantic Enhancement and Amplification Network (SEANet), a framework...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/10/3078 |
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| Summary: | Detecting underwater objects is a complex task due to the inherent challenges of low contrast and intricate backgrounds. The wide range of object scales further complicates detection accuracy. To address these issues, we propose a Semantic Enhancement and Amplification Network (SEANet), a framework designed to enhance underwater object detection in complex visual scenarios. SEANet integrates three core components: the Multi-Scale Detail Amplification Module (MDAM), the Semantic Enhancement Feature Pyramid (SE-FPN), and the Contrast Enhancement Module (CEM). MDAM expands the receptive field across multiple scales, enabling the capture of subtle features that are often masked by background similarities. SE-FPN combines multi-scale features, optimizing feature representation and improving the synthesis of information across layers. CEM incorporates Fore-Background Contrast Attention (FBC) to amplify the contrast between foreground and background objects, thereby improving focus on low-contrast features. These components collectively enhance the network’s ability to effectively identify critical underwater features. Extensive experiments on three distinct underwater object detection datasets demonstrate the efficacy and robustness of SEANet. Specifically, the framework achieves the highest <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>A</mi><mi>P</mi></mrow></semantics></math></inline-formula> (Average Precision) of 67.0% on the RUOD dataset, 53.0% on the URPC2021 dataset, and 71.5% on the DUO dataset. |
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| ISSN: | 1424-8220 |