一种改进的Faster R-CNN遥感图像多目标检测模型研究

doi:10.19678/j.issn.1000-3428.0068856

摘要/Abstract

摘要：

针对遥感图像背景复杂、目标种类多和尺度差异大所造成的目标漏检和误检问题，提出一种改进Faster R-CNN多目标检测模型。首先，采用Swin Transformer来替代ResNet 50骨干网络，增强模型特征提取能力；其次，添加平衡特征金字塔(BFP)模块融合浅层和高层语义信息，进一步加强特征融合效果；最后，在分类和回归分支中，添加动态权重机制，促进网络在训练过程中更关注高质量候选框，提高目标定位和分类的精确度。在RSOD数据集上的实验结果表明，所提模型相较于Faster R-CNN模型每秒浮点运算次数(FLOPs)大幅度减少，并且模型的mAP@0.5 ∶0.95提高了10.7百分点，平均召回率提高10.6百分点。相较于其他主流检测模型，所提模型在降低漏检率的同时，取得了更高的精度，能显著提高复杂背景下遥感图像的检测精度。

关键词: 遥感图像, 多目标检测, Faster R-CNN, Swin Transformer模块, 平衡特征金字塔, 动态权重机制

Abstract:

The complex backgrounds, diverse target types, and significant scale variations in remote sensing images lead to target omission and false detection. To address these issues, this study proposes an improved Faster R-CNN multi-object detection model. First, the ResNet 50 backbone network is replaced with the Swin Transformer to enhance the model's feature extraction capability. Second, a Balanced Feature Pyramid (BFP) module is introduced to fuse shallow and deep semantic information, further strengthening the feature fusion effect. Finally, in the classification and regression branches, a dynamic weighting mechanism is incorporated to encourage the network to focus more on high-quality candidate boxes during training, thereby improving the precision of target localization and classification. The experimental results on the RSOD dataset show that the proposed model significantly reduces the number of Floating-Point Operations per second (FLOPs) compared to the Faster R-CNN model. The proposed model achieves 10.7 percentage points improvement in mAP@0.5 ∶0.95 and 10.6 percentage points increase in Average Recall (AR). Compared to other mainstream detection models, the proposed model achieves higher accuracy while reducing the false detection rate. These results indicate that the proposed model significantly enhances detection accuracy in remote sensing images with complex backgrounds.

Key words: remote sensing images, multi-object detection, Faster R-CNN, Swin Transformer module, Balanced Feature Pyramid(BFP), dynamic weighting mechanism

苗茹, 李祎, 周珂, 张俨娜, 常然然, 孟更. 一种改进的Faster R-CNN遥感图像多目标检测模型研究[J]. 计算机工程, 2025, 51(8): 292-304.

MIAO Ru, LI Yi, ZHOU Ke, ZHANG Yanna, CHANG Ranran, MENG Geng. A Study on Improved Faster R-CNN Model for Multi-Object Detection in Remote Sensing Images[J]. Computer Engineering, 2025, 51(8): 292-304.

https://www.ecice06.com/CN/Y2025/V51/I8/292

图/表 28

图1 总体技术流程

Fig.1 Procedure of overall technical

图2 FSBD R-CNN网络架构

Fig.2 Architecture of FSBD R-CNN network

图3 Swin Transformer模块

Fig.3 Swin Transformer module

图4 基于移动窗口的多头自注意力操作

Fig.4 Multi-head self-attention operation based on shifted window

图5 平衡特征金字塔模块

Fig.5 Balanced feature pyramid module

图6 特征金字塔联合平衡特征金字塔的结构

Fig.6 Structure of feature pyramid combined with balanced feature pyramid

图7 动态标签分配流程

Fig.7 Procedure of dynamic label assignment

图8 L1Loss与回归误差曲线

Fig.8 Curve of L1Loss and regression error

图9 不同β值的SmoothL1损失与回归误差曲线

Fig.9 Curves of SmoothL1 loss and regression error for different β values

图10 RSOD数据集示例

Fig.10 Examples of RSOD dataset

图11 目标种类标签数量

Fig.11 Number of object class labels

图12 Loss函数随着迭代次数的变化曲线

Fig.12 The change curves of the Loss function with the number of iterations

图13 改进前后网络模型P-R曲线对比

Fig.13 Comparison of P-R curves of network models before and after improvement

图14 不同模型对语义信息不明显物体的检测结果

Fig.14 The detection results of objects with unclear semantic information by different models

图15 目标漏检错检结果对比

Fig.15 Comparison of target missed detection and false detection results

图16 复杂背景检测结果对比

Fig.16 Comparison of detection results in complex backgrounds

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