基于[KG-*9]Ghost卷积与注意力机制的SAR图像建筑物检测算法

针对深度卷积神经网络存在模型参数量大、占用内存资源等问题，提出了一种基于轻量化网络的SAR图像建筑物检测算法。首先以旋转目标检测算法R-centernet为基础，将主干网络中的传统卷积替换为Ghost卷积，并构建Ghost-ResNet网络，降低模型参数量；其次提出了融合宽高信息的通道注意力模块，增强网络对图像中显著区域的精确定位能力；使用CARAFE上采样代替网络中的DCN模块，在上采样过程中充分结合特征图信息，提高目标检测能力；最后使用改进的R-centernet算法在旋转标注的SAR图像建筑物数据集上进行训练与测试。实验结果表明，相比于原始R-centernet算法，改进后的算法准确率提高了3.8%，召回率提高了1.2%，检测速度提高了12帧/s。

Building Detection Algorithm in SAR Images Based on Ghost Convolution and Attention Mechanisms

A building detection algorithm in SAR images based on a lightweight network is proposed to address the problems caused by the large quantity of model parameters and memory resources involved in deep convolutional neural networks. First, the algorithm is based on the rotating detector R-centernet, and the traditional convolution in backbone network is replaced by Ghost convolution. Then, the Ghost-ResNet is constructed to reduce the number of model parameters. Second, a channel attention module that fuses width and height information is developed to enhance the network's ability to locate significant regions in the images accurately. An up-sampling method named CARAFE is used to replace the DCN module in the network, and the feature map information is fully combined in the up-sampling process to improve target detection. Finally, the improved R-centernet is used to train and test the SAR rotating building dataset. Based on the experimental results, compared with the R-centernet, the improved algorithm has increased detection accuracy by 3.8%, recall by 1.2%, and detection speed by 12 frames per second.