基于改进YOLOv8算法的遥感图像目标检测

Remote-Sensing Image Object Detection Based on Improved YOLOv8 Algorithm

针对遥感图像目标检测算法漏检和误检率高、目标定位不精确、无法准确识别目标类别等问题,提出一种基于改进YOLOv8的目标检测算法。为提高模型的损失函数对梯度分配的灵活性,适应各种形状和尺寸的物体,设计了非单调聚焦机制与边界框几何因素相结合的边界框回归损失函数;为扩大模型的感受野并削弱遥感图像背景对检测目标的影响,采用全局注意力机制与残差块结合的方式,设计了残差全局注意力机制;为使模型适应遥感图像中目标物体的形变与不规则排列,对YOLOv8模型中的C2f模块进行改进,融入可变形卷积与可变形感兴趣区域池化层。实验结果表明,在DOTA数据集和RSOD数据集上,所提算法的平均精度均值(mAP@0.5)达到72.1%和94.6%,优于对比算法,提高了遥感图像目标检测精度,为遥感图像识别提供了新的手段。

A target detection algorithm based on improved YOLOv8 is proposed to address the issues of high-missed and false-detection rates,inaccurate target positioning,and inability to accurately identify target categories in remote-sensing image target detection algorithms.To improve the flexibility of the loss function of the model in gradient allocation and adapt to various object shapes and sizes,a boundary box regression loss function is designed,which combines a nonmonotonic focusing mechanism with geometric factors of the boundary box.To expand the receptive field of the model and weaken the influence of the remote-sensing image background on the detection target,a residual global attention mechanism is designed by combining global attention mechanism and residual blocks.To adapt the model to the deformation and irregular arrangement of target objects in remote-sensing images,the C2f module in the YOLOv8 model is improved by incorporating deformable convolution and deformable region-of-interest pooling layers.Experimental results show that on DOTA and RSOD datasets,mean average precision(mAP@O.5)of the improved YOLOv8 algorithm reaches 72.1%and 94.6%,which are better than other mainstream algorithms.It improves the accuracy of remote sensing image target detection and provides a new means for remote sensing image target detection.