摘要
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 expressive-ness.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 Shape-Loss 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.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Key words
marine benthos/underwater object detection/wavelet pooling/multi-scale feature fusion分类
信息技术与安全科学
