RFWNet: A Lightweight Remote Sensing Object Detector Integrating Multiscale Receptive Fields and Foreground Focus Mechanism

Yujie Lei;Wenjie Sun;Sen Jia;Qingquan Li;Jie Zhang
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Abstract

Challenges in remote sensing object detection (RSOD), such as high interclass similarity, imbalanced foreground–background distribution, and the small size of objects in remote sensing images, significantly hinder detection accuracy. Moreover, the tradeoff between model accuracy and computational complexity poses additional constraints on the application of RSOD algorithms. To address these issues, this study proposes an efficient and lightweight RSOD algorithm integrating multiscale receptive fields and foreground focus mechanism, named robust foreground weighted network (RFWNet). Specifically, we proposed a lightweight backbone network receptive field adaptive selection network (RFASNet), leveraging the rich context information of remote sensing images to enhance class separability. Additionally, we developed a foreground–background separation module (FBSM) consisting of a background redundant information filtering module (BRIFM) and a foreground information enhancement module (FIEM) to emphasize critical regions within images while filtering redundant background information. Finally, we designed a loss function, the weighted CIoU-Wasserstein loss ( $L_{\text {WCW}}$ ), which weights the IoU-based loss by using the normalized Wasserstein distance to mitigate model sensitivity to small object position deviations. The comprehensive experimental results demonstrate that RFWNet achieved 95.3% and 73.2% mean average precision (mAP) with 6.0 M parameters on the DOTA V1.0 and NWPU VHR-10 datasets, respectively, with an inference speed of 52 FPS.
RFWNet:一种集成多尺度感受场和前景聚焦机制的轻型遥感目标探测器
遥感目标检测存在类间相似性高、前景背景分布不平衡以及遥感图像中目标尺寸小等问题,严重影响了遥感目标的检测精度。此外,模型精度和计算复杂度之间的权衡给RSOD算法的应用带来了额外的限制。为了解决这些问题,本研究提出了一种集成多尺度感受野和前景聚焦机制的高效轻量级RSOD算法,称为鲁棒前景加权网络(robust前景加权网络,RFWNet)。具体而言,我们提出了一种轻量级的骨干网络接收场自适应选择网络(RFASNet),利用遥感图像丰富的上下文信息增强类可分性。此外,我们开发了由背景冗余信息过滤模块(BRIFM)和前景信息增强模块(FIEM)组成的前景背景分离模块(FBSM),以在过滤冗余背景信息的同时强调图像中的关键区域。最后,我们设计了一个损失函数,加权的CIoU-Wasserstein损失($L_{\text {WCW}}$),它通过使用归一化的Wasserstein距离来加权基于iou的损失,以减轻模型对小目标位置偏差的敏感性。综合实验结果表明,在DOTA V1.0和NWPU VHR-10数据集上,RFWNet在6.0 M参数下的平均精度(mAP)分别达到95.3%和73.2%,推理速度为52 FPS。
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