Improved YOLOv8 for Remote Sensing Image Detection
An improved YOLOv8 remote sensing image detection algorithm is proposed to address the issues of false detection,missed detection,and low detection accuracy in current remote sensing image object detection algorithms.Attention mechanism EMA is introduced into the C2f module in the backbone network to improve the model's feature extraction ability for multi-scale objects.A Slim-PAN structure is proposed in the neck network to reduce the model computational complexity.WIOU loss function is used instead of CIOU loss function to improve the detection accuracy of the model.The experimental results on DIOR and RSOD remote sensing datasets show that compared with the original YOLOv8 algorithm,the mAP of the improved algorithm is increased by 1.5％and 2.3％respectively,and the calculation amount is reduced by 0.3 GFLOPs,which reflects that the improved algorithm can improve the detection accuracy without increasing the calculation amount,and the effectiveness and advancement of the improved algorithm is proved.
万方数据
维普
