Cascade detection method of millimeter wave image human body carried object

Abstract

The invention belongs to the technical field of image processing, and particularly relates to a cascade detection method of a millimeter wave image human body carried object. In order to solve the problem that a human body carrying object in a millimeter wave image is small, the cascade detection method adopts a Top-down structure to obtain context information of the millimeter wave image, and completes positioning and recognition of a small target through a context relation; in order to solve the problem that a positive sample in a millimeter wave image is sparse, the cascade detection methodadopts a cascade model mode, utilizes a cascade model of the first stage to filter a negative sample while adjusting the coordinate positions of initialized candidate frames of the model so as to provide effective candidate frame information for the cascade model of the second stage; and based on candidate frames with balanced proportion of positive and negative samples and accurate coordinate positions, the cascade model of the second stage further improves the detection rate of the model and reduces the false alarm rate of the model.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for detecting objects carried by a human body. Background technique [0002] The development of millimeter wave (MMW) sensing technology has made it an important part of the field of security and security inspection. It is different from traditional metal detection technology and can penetrate human clothing without causing harm to the human body. In recent years, the millimeter-wave holographic imaging system [1] developed by Shanghai Institute of Microsystems, Chinese Academy of Sciences can obtain millimeter-wave images with higher resolution, making it possible to automatically identify prohibited objects carried by the human body in millimeter-wave images. [0003] Millimeter wave imaging systems [2] can be divided into passive millimeter wave imaging (PMMW) and active millimeter wave imaging (AMMW). Since the thermal radiation of different tar...

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Problems solved by technology

[5] uses the target detector in the natural image, and uses transfer learning to combine the Faster-RCNN detector with the AMMW image, which can effectively detect potential prohibited objects in the [1] dataset, but due to the Faster-RCNN The RPN[7] operation only considers the image in the window and lacks global information such as human body context, so it often has a high false positive rate in practical applications

[0024] Since too small foreground targets will gradually disappear during the downsampling process of CNNs, it is difficult to accurately identify and locate small targets only by relying on the appearance characteristics of the small target itself

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