Multi-Scale Feature Fusion Enhancement for Underwater Object Detection

Autor:	Zhanhao Xiao, Zhenpeng Li, Huihui Li, Mengting Li, Xiaoyong Liu, Yinying Kong
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	underwater object detection DETR cross-scale feature fusion Chemical technology TP1-1185
Zdroj:	Sensors, Vol 24, Iss 22, p 7201 (2024)
Druh dokumentu:	article
ISSN:	1424-8220
DOI:	10.3390/s24227201
Popis:	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.
Databáze:	Directory of Open Access Journals
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