基于改进YOLOv4的遥感图像目标检测方法

Object Detection Method Based on Improved YOLOv4 Network for Remote Sensing Images

遥感图像存在背景复杂、目标小且密集排列等问题,基于深度学习的目标检测方法可以提高目标检测的准确率,但是普遍存在模型参数量较多、检测速度一般的问题.针对上述问题,提出一种基于改进YOLOv4的遥感图像目标检测方法.首先采用轻量化网络Mobile NetV3代替YOLOv4的原特征提取网络,提高检测速度;其次在预测层中串联自注意力机制,使用改进非极大值抑制算法进行后处理;最后,在图像预处理中通过Mosaic方法进行数据增强,使用K-means方法获得更匹配遥感目标的候选框参数,在预测层中使用Complete Intersection Over Union(CIoU)损失函数进行坐标框定位.实验数据集由NWPUVHR-10和DOTA两个经典遥感数据集共同组成,包含船、车辆、港口等10个类别.结果表明,当遥感图像输入尺寸为608×608时,检测速度为54 frame/s,是YOLOv4检测速度的1.6倍,平均精度均值达到85.60%,所提方法在保持较高检测精度的同时,减小了参数量、提高了检测速度.

Remote sensing images have many problems,such as complex background,small targets,and dense arrangement.The target detection method based on depth learning can improve the accuracy of target detection,but there are many problems,such as more model parameters and general detection speed.Aiming at the above problems,a remote sensing image target detection method based on improved YOLOv4 is proposed.First,the lightweight network Mobile NetV3 is used to replace the original feature extraction network of YOLOv4 to improve the detection speed;second,the selfattention mechanism is concatenated in the prediction layer,and the improved non maximum suppression algorithm is used for postprocessing;finally,in the image preprocessing,Mosaic method is used to enhance the data,Kmeans method is used to obtain the candidate frame parameters that better match the remote sensing target,and Complete Intersection Over Union(CIoU)loss function is used in the prediction layer to locate the coordinate frame.The experimental data set consists of two classical remote sensing datasets,NWPUVHR-10 and DOTA,including 10 categories of ships,vehicles,and ports.The results show that when the input size of remote sensing image is 608×608,the detection speed is 54 frame/s,1.6 times that of YOLOv4,and the average accuracy is 85.60%.The proposed method reduces the parameter amount and improves the detection speed while maintaining a high detection accuracy.