The millimeter-wave is an electromagnetic wave without ionizing radiation. It can penetrate the insulating cloth and is harmless to the human body. These characteristics make the millimeter wave have a wide range of application prospects in the field of public safety. Apply deep learning to the field of millimeter-wave image object detection, a millimeter-wave image object detection method based on improved YOLOv3-Tiny is proposed. Firstly, add convolutional layers to the feature extraction network to increase the depth of the network and increases to 3 different scale prediction layers to enhance the detection ability of millimeter-wave image object. Then, the Convolutional block attention module is introduced in the Feature pyramid network to make the network pay more attention to the features of targets and ignore the characteristics of redundant background noise. The results show that the improved network has mean average accuracy up to 93.4%, single frame detection speed is 15 ms, model parameters are only 38.7M, which provides a reference value for the research of high precision and miniaturization of millimeter wave security system.