Computer Science

CLC number: TP751

Improved YOLOX Remote Sensing Image Object Detection Algorithm

改进YOLOX的遥感图像目标检测算法

College of Electronic and Information, Southwest Minzu University, Chengdu 610041, Sichuan, China

^† Corresponding author. E-mail: 21500059@swun.edu.cn

Received: 1 September 2023

Abstract

Remote sensing image object detection is one of the core tasks of remote sensing image processing. In recent years, with the development of deep learning, great progress has been made in object detection in remote sensing. However, the problems of dense small targets, complex backgrounds and poor target positioning accuracy in remote sensing images make the detection of remote sensing targets still difficult. In order to solve these problems, this research proposes a remote sensing image object detection algorithm based on improved YOLOX-S. Firstly, the Efficient Channel Attention (ECA) module is introduced to improve the network's ability to extract features in the image and suppress useless information such as background; Secondly, the loss function is optimized to improve the regression accuracy of the target bounding box. We evaluate the effectiveness of our algorithm on the NWPU VHR-10 remote sensing image dataset, the experimental results show that the detection accuracy of the algorithm can reach 95.5%, without increasing the amount of parameters. It is significantly improved compared with that of the original YOLOX-S network, and the detection performance is much better than that of some other mainstream remote sensing image detection methods. Besides, our method also shows good generalization detection performance in experiments on aircraft images in the RSOD dataset.

摘要

遥感图像目标检测是遥感图像处理的核心任务之一。近年来, 随着深度学习技术的发展, 遥感图像中的目标检测技术取得了很大的进步。然而, 遥感图像中存在的小目标密集、背景复杂以及目标定位精度差等问题, 使得遥感目标的检测仍然比较困难。为了解决这些问题, 本文提出了一种基于改进YOLOX-S的遥感图像目标检测算法。首先, 引入高效通道注意力模块ECA, 提升网络对图像中重要特征的提取能力, 抑制背景等无用信息; 其次, 优化损失函数,提升目标边界框的回归精度。我们在公开的遥感图像数据集NWPU VHR-10上评估了本文算法的有效性, 实验结果表明该算法的检测精度能达到95.5%, 在不增加参数量的情况下, 较原有YOLOX-S网络有明显提升, 且检测性能大幅优于其他一些主流的遥感图像检测方法。除此之外, 在RSOD数据集中的飞机图像的泛化性实验中, 我们的方法也表现出不错的检测性能。