Underwater occlusion object recognition with fusion of significant environmental features

Zhou, Jiyong; Xu, Tao; Guo, Wenda; Zhao, Weishuo; Cai, Lei

doi:10.1117/1.jei.31.2.023016

Cited by 6 publications

(1 citation statement)

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“…YOLOv3-Marine has improved the network structure of YOLOv3, reduced network parameters, and increased detection speed. Aiming at underwater object feature loss caused by underwater object occlusion, Zhou et al 26 constructed a contrast graph structure of object salient features and relevant environmental features and proposed an underwater occlusion target recognition algorithm combined with salient environmental features. Chen et al 27 proposed a new neural network, SWIPENet, aimed at the problems of underwater image blur, small targets, and noise.…”

Section: Underwater Object Detectionmentioning

confidence: 99%

MFDNN: multi-scale feature-weighted dual-neck network for underwater object detection

Xu,

Zhang,

Tong

2024

J. Electron. Imag.

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With the rapid development of object detection technology, underwater object detection has gradually become a hot topic. Due to the complex underwater environment, some object detection algorithms still encounter difficulties in detecting targets similar to the background. For the above problems, we propose a multi-scale featureweighted dual-neck network (MFDNN) for underwater object detection. Our contribution is mainly divided into three parts. First, an enhanced feature extraction network, namely the dual-neck network, is designed to process and reuse the features extracted from the backbone network. Second, an attention mechanism is embedded in one of the neck networks to reweight features and pay more attention to important features. In addition, we introduce the adaptively spatial feature fusion mechanism to adaptively weight the features extracted at multiple scales. As demonstrated in comprehensive experiments, the mean average precision of our MFDNN can reach 87.79% and 86.51% on the underwater datasets URPC2019 and URPC2020, respectively.

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Section: Underwater Object Detectionmentioning

confidence: 99%