RFWNet: A Lightweight Remote Sensing Object Detector Integrating Multi-Scale Receptive Fields and Foreground Focus Mechanism

• URL: http://arxiv.org/abs/2503.00545v1
• Date: Sat, 01 Mar 2025 16:02:15 GMT
• Title: RFWNet: A Lightweight Remote Sensing Object Detector Integrating Multi-Scale Receptive Fields and Foreground Focus Mechanism
• Authors: Yujie Lei, Wenjie Sun, Sen Jia, Qingquan Li, Jie Zhang,
• Abstract summary: This study proposes an efficient and lightweight RSOD algorithm integrat-ing multi-scale receptive fields and foreground focus mechanism, named RFWNet.<n> Experimental evaluations on the DOTA V1.0 and NWPU VHR-10 datasets demonstrate that RFWNet achieves advanced perfor-mance with 6.0M parameters and can achieves 52 FPS.
• Score: 10.997183129304409
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Challenges in remote sensing object detection (RSOD), such as high inter-class similarity, imbalanced foreground-background distribution, and the small size of objects in remote sensing images significantly hinder detection accuracy. Moreo-ver, the trade-off 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 integrat-ing multi-scale receptive fields and foreground focus mechanism, named RFWNet. Specifically, we proposed a lightweight backbone network Receptive Field Adaptive Selection Network (RFASNet), leveraging the rich context infor-mation 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 and a foreground information enhancement module to emphasize critical regions within images while filtering redundant background information. Finally, we designed a loss function, the Weighted CIoU-Wasserstein (WCW) loss, which weights the IoU-based loss by using the Normalized Wasserstein Distance to mitigate model sensitivity to small object position deviations. Experimental evaluations on the DOTA V1.0 and NWPU VHR-10 datasets demonstrate that RFWNet achieves advanced perfor-mance with 6.0M parameters and can achieves 52 FPS.

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