改进YOLOv8的水面小目标检测算法

Improved YOLOv8 Algorithm for Small Object Detection on Water Surface

针对水面目标检测中的噪声干扰和小目标的漏检问题,提出一种改进YOLOv8的水面小目标检测算法YOLOv8-WSSOD(YOLOv8-water surface small object detection).首先,为降低水面复杂环境在主干网络下采样过程中产生的噪声干扰,提出基于BiFormer双层路由注意力机制构建的C2fBF(C2f-BiFormer)模块,在特征提取过程中保留细粒度的上下文特征信息;其次,针对水面小目标的漏检问题,新增一个更小的检测头,提升网络对小目标的感知力,并在Neck端引入GSConv和Slim-neck,减轻模型复杂度并保持精度;最后,使用MPDIoU损失函数解决CIoU损失函数的局限性,以提高模型检测准确率.实验结果表明,相较于原始YOLOv8算法,该算法在水面小目标上平均准确率mAP@0.5提升了4.6%,mAP@0.5:0.95提升了2.2%,并且改进后的算法检测速度达到86f/s,能有效实现对水面小目标快速、准确的检测.

To address the problems of noise interference and missed detection of small objects in water surface object detection,this study proposes an improved You Only Look Once version 8(YOLOv8)algorithm for water surface small object detection,namely,YOLOv8-WSSOD.Specifically,to reduce the noise interference caused by the complex water surface environment during the downsampling in the backbone network,the study proposes the C2f-BiFormer(C2fBF)module constructed based on BiFormer’s bi-level routing attention mechanism to retain fine-grained contextual feature information during feature extraction.Then,as to the missed detection of small objects on the water surface,a smaller detection head is added to enhance the network’s sensitivity to small objects.At the Neck end,the ghost-shuffle convolution(GSConv)and Slim-neck structures are used to reduce the model’s complexity and maintain precision.Finally,the limitations of the complete intersection over union(CIoU)loss function are overcome by the minimum point distance-based IoU(MPDIoU)loss function to improve the model’s detection precision.The experimental results show that compared with the original YOLOv8 algorithm,the proposed algorithm increases the mean average precision mAP@0.5 and mAP@0.5:0.95 on small objects on the water surface by 4.6%and 2.2%,respectively.Furthermore,the modified algorithm,achieving a detection speed of 86 f/s,is readily available for fast and accurate detection of small objects on the water surface.