A Remote Sensing Target Detection Algorithm Based on Improved YOLOV4
ZHANG Lixia
MA Zhiming
LIU Zhandong
PENG Xiangshu
Abstract：Aiming at the multi-scale,diversity and complex background of remote sensing targets,in order to improve the de-tection speed and average accuracy of YOLOv4 algorithm,a new algorithm for remote sensing object detection based on YOLOv4 model is proposed.Firstly,the backbone feature extraction network of YOLOv4 is replaced with Mobilenetv2 to reduce the number of parameters and improve the detection speed.Secondly,novel attention mechanism CoordAttention modules embedded in Mobile-netv2's residual network capture information on sense of orientation and position perception,accurately locate and identify targets of interest.Finally,based on the idea of Inception,an improved RFB module is added to the neck feature enhancement network to en-hance the receptive field and enhance the feature fusion capability of the network.It is shown that the proposed MCR-YOLOv4(Mo-bilenetv2-CoordAttention-RFB-You Only Look Once)algorithm reduces the model size in 45.27 M,1.03%improvement in aver-age accuracy and 56 frames/s compared to the original YOLOv4 algorithm,and is more suitable for the detection of complex remote sensing targets.
Keywords：digital image processingMobilenetv2remote sensing targettarget detectionYOLOv4
Publication Date：2024-10-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 2863-2868,2896 )