ALF-YOLO: Enhanced YOLOv8 based on multiscale attention feature fusion for ship detection

Wang, Siwen; Li, Ying; Qiao, Sihai

doi:10.1016/j.oceaneng.2024.118233

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2024

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Cited by 5 publications

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Asymmetric Aggregation Network for Accurate Ship Detection in Optical Imagery

Zhang,

2024

IEEE Trans. Geosci. Remote Sensing

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Asymmetric Aggregation Network for Accurate Ship Detection in Optical Imagery

Zhang,

2024

IEEE Trans. Geosci. Remote Sensing

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Indoor Fire Detection Based on Improved YOLOv8

杨

2024

MOS

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AFF-LightNet: Attentional Feature Fusion Based Lightweight Network for Ship Detection

Yuan,

Yu,

Rong

et al. 2024

Preprint

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Efficient mobile detection equipment plays a vital role in ensuring maritime safety, and accurate ship identification is crucial for maritime traffic. Recently, advanced learning-based methods boost the accuracy of ship detection, but face challenges on mobile devices due to size and computation. Thus, we propose a lightweight ship detection network based on feature fusion, called AFF-LightNet. We introduce iterative attentional feature fusion (IAFF) into the proposed neck network, improving the efficiency of feature fusion by introducing a multi-scale channel attention module. Also, Conv is replaced by DCNv2 in the backbone network to further improve the detection accuracy of the proposed network. DCNv2 enhances the spatial sampling position in convolution and Rol pooling by introducing offsets. Moreover, a lightweight convolution GhostConv was introduced into the head network to reduce the number of parameters and computation cost. Last, SIOU was leveraged to improve the convergence speed of the model. We conduct extensive experiments on the publicly available dataset SeaShips and compare it with existing methods. The experimental results show that compared with the standard YOLOv8n, the improved network has an average accuracy of 98.8%, an increase of 0.4%, a reduction of 1.9 G in computational complexity, and a reduction of 0.19 M in parameter count.

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ALF-YOLO: Enhanced YOLOv8 based on multiscale attention feature fusion for ship detection

Cited by 5 publications

References 44 publications

Asymmetric Aggregation Network for Accurate Ship Detection in Optical Imagery

Asymmetric Aggregation Network for Accurate Ship Detection in Optical Imagery

Indoor Fire Detection Based on Improved YOLOv8

AFF-LightNet: Attentional Feature Fusion Based Lightweight Network for Ship Detection

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