A small sample terahertz image foreign matter detection method based on integrated deep learning

A foreign object detection and deep learning technology, applied in the field of image processing, can solve the problems of increasing the number of samples, cumbersome foreign object detection process, and high computational complexity in the modeling process, achieving high foreign object detection accuracy, improving detection accuracy, and training process. simple effect

CN109948527AActive Publication Date: 2019-06-28XIDIAN UNIV
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
Publication Date
2019-06-28

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Abstract

The invention discloses a small sample terahertz image foreign matter detection method based on integrated deep learning, and mainly solves the problems that an existing method needs to manually design image features, the training process is complex, and foreign matter detection cannot be carried out on a small sample terahertz image with a small sample number. The method comprises the following specific steps: (1) making a small sample terahertz image data set; (2) amplifying the image training set; (3) constructing an integrated deep learning network; (4) training the integrated deep learning network; and (5) detecting the image test set. According to the method, the image features can be automatically extracted, the training process is simple, the situation that a certain type of samples in actual samples are particularly few is considered, foreign matter detection can be conducted on the terahertz images of the small samples, and the detection accuracy of the certain type of samples in the small samples is particularly few can be improved.
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Description

Technical field

[0001] The present invention belongs to the technical field of image processing, and further relates to a small sample terahertz image foreign body detection method based on integrated deep learning in the technical field of target detection image processing. The invention can be used to detect foreign objects such as knives, explosives, and mobile phones hidden by the human body from the terahertz image. Background technique

[0002] In recent years, with the frequent occurrence of terrorist attacks at home and abroad, rapid and efficient human foreign body detection in crowded public places such as airports, railway stations, and subways has become an urgent need under the current severe security situation. Because terahertz wave is between infrared and microwave, it has some special properties that X-ray, light wave / infrared and microwave do not possess, making terahertz imaging very suitable for human body foreign body detection. Compared with traditional met...

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Embodiment Construction

[0035] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0036] Reference attached figure 1 , The steps of the present invention will be described in further detail.

[0037] Step 1. Make a small sample terahertz image data set.

[0038] Input 1000 terahertz images, according to the standard visual object classification VOC (Visual Object Classes) data set format, make a small sample terahertz image data set.

[0039] Mark the category and coordinate position of each image target in the small sample terahertz image data set. The category belongs to five categories: people, knives, mobile phones, explosives, and suspects.

[0040] Randomly select 80% of the terahertz images from the small sample terahertz image data set to form an image training set, and combine the remaining terahertz images into an image test set.

[0041] Step 2. Amplify the image training set.

[0042] Use the brightness and contrast formula to adjust the b...