基于改进快速区域卷积网络的目标检测轻量化算法

基于深度学习的目标检测算法已成为合成孔径雷达（SAR）图像目标检测任务的主流。深层网络通常具有大量参数，运行速度不能满足实时要求，难以在资源受限的设备(如移动端)上部署。考虑到对模型实时性和可移植性的要求，对双阶段目标检测算法快速区域卷积神经网络进行轻量化改进，比较不同改进方法对算法速度与精度的影响。结合SAR图像的特点，优化轻量化模型，与单阶段目标检测算法的单脉冲多盒检测网络对比。仿真实验结果表明，改进轻量化模型在保持原有精度水平下，模型占用内存和算法运算量大大减少，可有效满足SAR图像目标检测的实时性要求。

A Lightweight Target Detection Algorithm Based on the Improved Faster-RCNN

The target detection algorithms based on deep learning has become the mainstream of target detection in synthetic aperture radar images. Deep network algorithm often has a large number of parameters and don't run fast enough to meet real-time requirements，making it difficult to deploy on resource-constrained devices such as mobile terminal. Considering the requirements of real-time performance and portability of the model，Faster-RCNN for the two-stage target detection algorithm was improved to compare the influence of different improved methods on the speed and accuracy of algorithm.The lightweight model was optimized in combination with the characteristics of synthetic aperture radar (SAR) images，and finally compared with the single shot multibox detector for one-stage target detection algorithm.The experimental results show that the speed of the improved lightweight model is greatly improved while maintaining the original accuracy level，which can effectively meet the real-time requirements of SAR image target detection.