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计算机系统应用英文版:2024,33(11):177-185
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改进YOLOv8的水下目标检测
(1.南京信息工程大学 自动化学院, 南京 210044;2.江苏省大气环境与装备技术协同创新中心, 南京 210044)
Underwater Target Detection via Improved YOLOv8
(1.School of Automation, Nanjing University of Information Science & Technology, Nanjing 210044, China;2.Jiangsu Collaborative Innovation Center for Atmospheric Environment and Equipment Technology, Nanjing 210044, China)
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Received:April 24, 2024    Revised:May 29, 2024
中文摘要: 针对水下目标检测中的尺度不一、重叠遮挡目标的漏检问题, 提出了一种改进的YOLOv8水下目标检测算法. 首先, 在主干网络中引入可变形卷积 (deformable convolution network, DCN), 通过卷积核自适应形变的机制, 提高模型对重叠遮挡目标的特征提取能力; 其次, 设计了一种空洞卷积空间金字塔模块(atrous spatial pyramid faster, ASPF), 扩大输出特征图的感受野, 提高模型对水下多尺度目标的感知能力; 最后, 对损失函数进行改进, 优化模型的训练过程并提高定位精度. 将改进算法在URPC数据集上进行实验, 结果表明改进算法的检测精度达到了87.3%, 相较于原始算法YOLOv8提高了3.4%, 同时能够精准检测水下多尺度、重叠遮挡目标.
Abstract:An improved YOLOv8 algorithm for underwater target detection is proposed to prevent missed detection of objects with different scales and overlapping occlusion. Firstly, deformable convolutions are introduced into the backbone network (deformable convolution network, DCN) to improve the feature extraction capability of the model by means of the adaptive deformation mechanism of convolution kernels. Secondly, a module combining atrous convolution and spatial pyramid, termed ASPF, is designed to expand the receptive field of the output feature map and improve the perception ability of the model for detecting underwater targets of multiple scales. Finally, the loss function is improved to optimize the training process of the model and improve detection accuracy. The improved algorithm is tested on the URPC data set, and the results show that its detection accuracy reaches 87.3%, which is 3.4% higher than that of the original YOLOv8 algorithm. Moreover, it can accurately detect underwater targets with different scales and overlapping occlusion.
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基金项目:国家自然科学基金(61671010); 青蓝工程(2191091900101)
引用文本:
周昕,李远禄,吴明轩,范小婷,王键翔.改进YOLOv8的水下目标检测.计算机系统应用,2024,33(11):177-185
ZHOU Xin,LI Yuan-Lu,WU Ming-Xuan,FAN Xiao-Ting,WANG Jian-Xiang.Underwater Target Detection via Improved YOLOv8.COMPUTER SYSTEMS APPLICATIONS,2024,33(11):177-185