Multi-Scale Feature Fusion Enhancement for Underwater Object Detection

Underwater object detection (UOD) presents substantial challenges due to the complex visual conditions and the physical properties of light in underwater environments. Small aquatic creatures often congregate in large groups, further complicating the task. To address these challenges, we develop Aqu...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2024-11, Vol.24 (22), p.7201
Main Authors: Xiao, Zhanhao, Li, Zhenpeng, Li, Huihui, Li, Mengting, Liu, Xiaoyong, Kong, Yinying
Format: Article
Language:English
Subjects:
Accuracy
Computational linguistics
cross-scale feature fusion
Datasets
Design
DETR
Efficiency
Language processing
Methods
Natural language interfaces
Neural networks
Semantics
Sensors
underwater object detection
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Summary:Underwater object detection (UOD) presents substantial challenges due to the complex visual conditions and the physical properties of light in underwater environments. Small aquatic creatures often congregate in large groups, further complicating the task. To address these challenges, we develop Aqua-DETR, a tailored end-to-end framework for UOD. Our method includes an align-split network to enhance multi-scale feature interaction and fusion for small object identification and a distinction enhancement module using various attention mechanisms to improve ambiguous object identification. Experimental results on four challenging datasets demonstrate that Aqua-DETR outperforms most existing state-of-the-art methods in the UOD task, validating its effectiveness and robustness.
ISSN:1424-8220
1424-8220
DOI:10.3390/s24227201