摘要
Abstract
In the UAV aerial image target detection task,the traditional target detection algorithm is poor in real-time and accuracy.The orig-inal YOLO algorithm has a high error detection and omission rate for small targets.The requirements of aerial image are higher in view angle,image data amount,target scale and so on,which are significantly different from ordinary images.Therefore,an improved algorithm based on YOLO-v7,FCL-YOLO-v7,is proposed to solve the problem of small target detection in UAV aerial images.First,add small target detection layer,improve the feature extraction network structure and prior frame configuration;Secondly,the SiLU activation function is replaced by FReLU activation function.Thirdly,CBAM attention mechanism is added to the backbone network;Finally,the small target data set is con-structed by combining the open data set and the autonomous UAV aerial images.The experimental results show that the accuracy of the im-proved algorithm is 6.7%higher than that of the original algorithm and 7.3%higher than that of YOLO-v3.The recall rate is 3.3%higher than the YOLO-v5.关键词
无人机航拍图像/小目标检测/特征提取/激活函数/注意力机制Key words
UAV aerial image/small target detection/feature extraction/activation function/attention mechanism分类
信息技术与安全科学
