Remote-Sensing Image Object Detection Based on Improved YOLOv8 Algorithm
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@0.5)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.