YGNet: A Lightweight Object Detection Model for Remote Sensing

Xin Song;Erhao Gao
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Abstract

In the dynamic field of remote sensing images (RSIs), the challenge of object scale variability and sensor resolution disparities is formidable. Addressing these complexities, we have designed a lightweight remote sensing model named YGNet, tailored for multiscale object detection. It demonstrates excellent performance in detecting both multiscale and small objects within RSIs. The E-RMSK module within YGNet employs a gradient-based architecture with multiple parallel reparameterized convolutions in its internal branches, facilitating the extraction of multiscale features while maintaining parameter and computational efficiency. The HLS-PAN structure integrates feature maps extracted through feature selection, enabling the top layers to relay image information downward to lower levels and the lowest layers to transmit data upward for localization, achieving feature fusion. This synergistic effect of the module design enhances the accuracy of object detection in complex remote sensing scenarios and ensures the model’s feasibility on platforms with limited resources. Rigorous testing on the RSOD and NWPU VHR-10 datasets has proven YGNet’s exceptional capabilities, achieving the mean average precision (mAP) scores of 96.2% and 88.9%, respectively. The model meets the demands for real-time, lightweight, multiscale object detection in remote sensing imagery, making it highly suitable for deployment in resource-constrained environments.
YGNet:一个轻量级的遥感目标检测模型
在遥感图像动态领域中,地物尺度变异性和传感器分辨率差异是一个巨大的挑战。为了解决这些复杂问题,我们设计了一个轻量级的遥感模型,名为YGNet,专为多尺度目标检测而设计。该方法在多尺度和小尺度目标检测方面均表现出优异的性能。YGNet中的E-RMSK模块采用基于梯度的架构,在其内部分支中使用多个并行的重参数化卷积,便于在保持参数和计算效率的同时提取多尺度特征。HLS-PAN结构集成了通过特征选择提取的特征图,顶层向下传递图像信息到底层,底层向上传输数据进行定位,实现特征融合。这种模块设计的协同效应提高了复杂遥感场景下目标检测的精度,保证了模型在资源有限的平台上的可行性。在RSOD和NWPU VHR-10数据集上的严格测试证明了YGNet的卓越能力,平均精度(mAP)分别达到96.2%和88.9%。该模型满足遥感图像中实时、轻量化、多尺度目标检测的需求,非常适合在资源受限环境下部署。
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