基于改进YOLOv8的遥感图像飞机目标检测研究

Research on Aircraft Target Detection in Remote Sensing Images Based on Improved YOLOv8

为解决遥感图像飞机目标检测时易出现检测精度低与漏检误检等问题,提出了一种基于YOLOv8算法的遥感图像飞机目标检测改进算法。首先,将坐标注意力机制模块嵌入卷积模块中,使其能提取复杂背景下的飞机小目标;然后,优化了检测头,去除了大的目标检测头,在提升小目标检测能力的同时减少算法的计算量;最后,使用WIoU作为改进的损失函数,以提高检测精度。实验表明,改进的YOLOv8算法能够有效提高飞机检测精度,可适用于遥感图像的飞机目标检测。

To address the issues of low detection accuracy and missed detections in aircraft target detection in remote sensing images,an improved algorithm for aircraft target detection in remote sensing images based on the YOLOv8 algorithm is proposed.Firstly,embed the coordinate attention mechanism module into the convolutional module to extract small aircraft targets in complex backgrounds.Then,the detection head was optimized by removing large object detection heads,which improved the detection ability of small objects while reducing the computational complexity of the algorithm.Finally,using WIoU as an improved loss function to improve detection accuracy.The experiment shows that the improved YOLOv8 algorithm can effectively improve the accuracy of aircraft detection and is suitable for aircraft target detection in remote sensing images.