提出一种包含去模糊的空间变换区域卷积神经网络的目标检测算法.首先，基于主动毫米波圆柱扫描成像原理对人体进行三维成像（频率24~30 GHz），建立毫米波图像数据集.然后，估计毫米波图像的模糊核，通过卷积去噪网络获得图像先验知识，将其集成到半二次分裂的优化方法中，以实现非盲目去模糊.最后，由定位网络、网格生成器和采样网络三部分组成空间变换网络，将它融入到特征提取网络中，在去模糊后实现目标检测.通过该非盲目去模糊算法得到的图像的峰值信噪比可达27.49 dB，目标检测算法的平均精度可达80.9%.实验结果表明，与现有的先进方法相比，该方法可以有效地提高图像质量和检测精度，为毫米波图像中隐藏危险品的目标检测提供了新的技术支持.

An object detection algorithm of spatial-transformer regional convolutional neural network with deblurring was proposed. Firstly, based on the principle of active millimeter-wave cylindrical scanning imaging, the human body is three-dimensionally imaged (frequency range from 24 GHz to 30 GHz), and a millimeter wave image data set is established. Then the blur kernel of the millimeter-wave image is estimated. The image prior knowledge is obtained by the convolutional denoiser network and is integrated into an optimization method of half quadratic splitting to achieve non-blind deblurring. Finally, the spatial transform network, composed of a localization net, a grid generator, and a sampling network, is inserted into the feature extraction network to achieve object detection after deblurring. With the proposed non-blind deblurring algorithm, peak signal to noise ratio of the image can reach 27.49 dB. Mean average precision of object detection algorithm can reach 80.9%. The experimental results show that the image quality and detection accuracy can effectively be improved through the proposed method compared with some state-of-the-art methods. New technical support is provided for object detection of hidden dangerous goods in millimeter-wave images.