Underwater Object Detection Algorithm with Anti-Aliasing and Multi-Scale Feature Fusion

doi:10.3778/j.issn.1002-8331.2406-0282

Abstract

Abstract: To address the challenges of multi-scale object detection in complex underwater environments, an improved algorithm, WPS-YOLOv8, is proposed. The wavelet pooling convolution (WPConv) module is designed, which reduces the resolution of feature maps after channel compression through wavelet pooling technology. This effectively suppresses frequency aliasing artifacts caused by traditional downsampling, improving both feature extraction quality and expressiveness. The partial pointwise group shuffle convolution (PGConv) module is introduced. By combining partial convolution with pointwise convolution, this module reduces information redundancy while maintaining information exchange between channels, addressing the limitations of depthwise separable convolution and enhancing feature fusion. The ShapeLoss loss function is proposed, which comprehensively considers factors affecting the accuracy of multi-scale object detection. By integrating Shape-IoU and Shape-NWD loss measures, it effectively improves overall detection accuracy for multi-scale objects. Experimental results show that, compared to YOLOv8, WPS-YOLOv8 achieves a mean average precision (mAP) improvement of 8.6 and 4.4 percentage points on the URPC2018 and UTDAC2020 underwater datasets, respectively, demonstrating its outstanding performance in underwater multi-scale object detection.

Key words: marine benthos, underwater object detection, wavelet pooling, multi-scale feature fusion

摘要： 针对水下环境复杂性带来的多尺度目标检测挑战，提出了改进算法WPS-YOLOv8。设计了小波池化卷积模块（wavelet pooling convolution，WPConv），该模块通过小波池化技术降低通道压缩后特征图的分辨率，有效抑制了传统下采样过程中产生的频率混叠伪影，提升了特征提取质量和表达能力。提出了局部逐点分组重排卷积模块（partial pointwise group shuffle convolution，PGConv），该模块通过结合局部卷积与逐点卷积，能够在减少信息冗余的同时保持通道间的信息交互，弥补了深度可分离卷积的不足，增强了特征融合效果。提出了ShapeLoss损失函数，综合考虑影响不同尺度目标检测精度的因素，通过集成Shape-IoU和Shape-NWD两种损失测度，有效提升了对多尺度目标的总体检测精度。实验结果显示，相较于YOLOv8，WPS-YOLOv8在URPC2018和UTDAC2020水下数据集上的平均精度均值（mean average precision，mAP）分别提升了8.6和4.4个百分点，展现了其在水下多尺度目标检测中的出色表现。

关键词: 海洋底栖生物, 水下目标检测, 小波池化, 多尺度特征融合

WANG Shupeng, LI Fan. Underwater Object Detection Algorithm with Anti-Aliasing and Multi-Scale Feature Fusion[J]. Computer Engineering and Applications, 2025, 61(18): 209-217.

王书朋, 李凡. 抗混叠与多尺度特征融合的水下目标检测算法[J]. 计算机工程与应用, 2025, 61(18): 209-217.

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