基于改进YOLOv7-tiny的高效轻量遥感图像目标检测方法

Efficient and lightweight target detection method for remote sensing images based on improved YOLOv7-tiny

针对现有遥感图像目标检测方法在受资源限制的小型设备中检测精度不足问题,提出了一种基于改进YOLOv7-tiny算法的高效轻量遥感图像目标检测方法。首先,针对遥感图像小目标分布密集问题,设计了一种低跨度的上下文解耦检测头模块,通过融合深层和浅层特征分别实现目标检测的分类和回归任务,有效解决了遥感图像小目标漏检和误检的问题。同时,针对遥感图像目标多尺度问题,设计了一种并行级联注意力机制,通过并行三分支网络与空间注意力模块相结合,增强了网络对多尺度目标特征的提取能力。此外,引入Focal-EIoU损失函数,提高模型泛化能力。对模型进行了对比实验、消融实验、部署实验和泛化实验,结果表明,在DIOR-5s和NWPU VHR-10数据集上的检测精度分别达到了85.4%、90.6%,相较原模型分别提高了2.6%、1.7%。且模型大小仅为19.1 MB,检测速度为64.1 fps,验证了算法的有效性。

To address the issue of low detection accuracy in remote sensing image target detection methods for small devices with limited resources,an efficient and lightweight method based on the improved YOLOv7-tiny algorithm is proposed.To address the issue of dense distribution of small targets in remote sensing images,a low-span context decoupling detection head module is designed.This module fuses deep and shallow features to perform classification and regression tasks for target detection.It effectively solves the problems of leakage and misdetection of small targets in remote sensing images.Meanwhile,a parallel series attention mechanism is proposed to enhance the network’s ability to extract multi-scale target features for remote sensing image targets.This is achieved by combining the parallel three-branch network with the spatial attention module.Additionally,the model’s generalization ability is improved through the introduction of the Focal-EIoU loss function.The comparison experiments,ablation experiments,deployment experiments and generalization experiments were conducted on the model.Experimental results indicate that the detection accuracy on DIOR-5s and NWPU VHR-10 datasets improved by 2.6%and 1.7%,respectively,compared to the original model.The model size is only 19.1 MB,and the detection speed is 64.1 fps,verifying the algorithm’s effectiveness.