基于改进YOLOv5的遥感图像目标检测

doi:10.19678/j.issn.1000-3428.0067790

摘要/Abstract

摘要： 目前目标检测技术虽然已经趋于成熟,但是对遥感图像的检测仍存在不少挑战。针对遥感图像的背景复杂、目标尺度差异大、目标方向任意等特点造成目标检测精度低下的问题,提出一种基于改进YOLOv5的遥感图像目标检测算法。首先,构建一种联合注意力的多尺度特征增强网络,充分融合高低层特征,使特征层具有语义信息的同时包含丰富的细节信息,并在融合过程中利用设计的特征聚焦模块帮助模型选择关键特征,抑制无关信息。其次,使用感受野模块(RFB)对融合后的特征图进行更新,扩大特征图的感受野,减少特征信息损失。最后,对目标增加旋转角度,并采用圆形平滑标签将回归问题转化成分类问题,提高遥感目标定位的准确性。在用于航拍图像目标检测的大规模数据集(DOTA)上的实验结果表明,与YOLOv5算法相比,所提算法的交并比(IoU)为0.5和0.5~0.95时的平均精度均值(mAP@0.5和mAP@0.5：0.95)分别提高了7.3和3.3个百分点,能够明显提高复杂背景下遥感图像目标的检测精度,并改善对遥感目标的漏检和误检情况。

关键词: 目标检测, 遥感图像, 特征融合, 感受野模块, 圆形平滑标签

Abstract: Although target detection technology has advanced, many challenges still exist in the detection of remote-sensing images. An improved YOLOv5-based remote-sensing image target detection algorithm is proposed to address the issues of low target detection accuracy caused by complex backgrounds, large target scale differences, and arbitrary target orientation in remote-sensing images. First, a joint multiscale feature enhancement network with attention is constructed to fully fuse high-level and low-level features such that the feature layers contain semantic and rich detailed information. During the fusion process, the designed feature focusing module is used to help the model select key features and suppress irrelevant information. Second, a Receptive Field Block(RFB) is used to update the fused feature map and expand the receptive field of the feature map to reduce feature information loss. Finally, by adding rotation angles to the targets and using circular smooth labels to transform the regression problem into a classification problem, the accuracy of remote-sensing target localization is improved. The experimental results on the a large-scale Dataset for Object deTection in Aerial images(DOTA) show that compared with the YOLOv5 algorithm, the mean Average Precision(mAP) when the Intersection over Union(IoU) values of the proposed algorithm are 0.5 and 0.5-0.95(mAP@0.5 and mAP@0.5∶0.95) increase by 7.3 and 3.3 percentage points, respectively. This can significantly improve the detection accuracy of remote-sensing image targets in a complex background and improve the missing and false detection of remote-sensing targets.

Key words: target detection, remote-sensing image, feature fusion, Receptive Field Block(RFB), circular smooth label

中图分类号:

TP391

崔丽群, 曹华维. 基于改进YOLOv5的遥感图像目标检测[J]. 计算机工程, 2024, 50(4): 228-236.

CUI Liqun, CAO Huawei. Target Detection of Remote-Sensing Images Based on Improved YOLOv5[J]. Computer Engineering, 2024, 50(4): 228-236.

https://www.ecice06.com/CN/Y2024/V50/I4/228

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