The present disclosure relates to an intelligent prevention passage control system for an electronic device, which comprises a detection device for an electronic device, a traffic control device integrated with the detection device for the electronic device, an industrial control host, a server, an access controller and a management terminal inside the traffic control device, The present disclosure can more accurately judge violations by scoring violations, and can make the system more intelligent by accurately judging violations, thus realizing no manual attendance, solving the problems of personnel recruitment, shift attendance and personnel management, and saving a lot of labor cost. At the same time, the system can avoid the hidden dangers caused by human feelings. sense of responsibility and inertia brought b manual prevention, and ensure the consistent, accurate and reliable standards of detection work.

An automatic generation system of a training image and a method thereof are provided. The disclosure generates a training image and records the target category and the target position. The disclosure adds the target image to the container image as a candidate image, calculates a reliability of the candidate image, and repeatedly executes the process until the reliability of the candidate image meets a threshold condition for generating the training image. The disclosure is able to generate the training images automatically, and the recognition difficulty of the training image is adjustable by the user, so as to be suitable for customized recognition training.

A method for multi-target detection and an apparatus for multi-target detection are capable of detecting at least two targets in real time or near real time. The real-time detection or near real time detection can be achieved by at least one of a Recipe Group Approach, an End Member Grouping Approach, and a Pixelated Grouping Based Approach.

A method includes receiving distributed sensing information on one or more characteristics associated with a subject from sensors distributed between a starting location spaced from a resolution location and the resolution location, the resolution location including a targeted screening area, the characteristics being obtained from the sensors observing a plurality of candidate subjects including the subject; determining, based on the information on the characteristics, which category of a plurality of categories is to be associated with the subject for a targeted screening process to be performed in which respective categories correspond to different thresholds applied in the targeted screening process; and electronically communicating information for the category associated with the subject to an electronic device, to direct the subject to a location of the targeted screening area which corresponds to the category, for performing the targeted screening process of the subject corresponding to a threshold associated with the category.

System and methodology are disclosed for approximating traditional SAR imaging on mobile mmWave devices. The presently disclosed technology enables human-perceptible and machine-readable shape generation and classification of hidden objects on mobile mmWave devices. The resulting system and corresponding methodology are capable of imaging through obstructions, like clothing, and under low visibility conditions. To this end, the presently disclosed technology incorporates a machine-learning model to recover the high-spatial frequencies in the object to reconstruct an accurate 2D shape and predict its 3D features and category. The technology is disclosed in particular for security applications, but the broader model disclosed is adaptable to different applications, even with limited training samples.

It is an object of the present invention to provide an object collating device and an object collating method that enable matching of images of a dividable medical article with desirable accuracy and easy confirmation of matching results. In the object collating device according to the first aspect, when the object is determined to be divided, the first image for matching is collated with the image for matching (the second matching image) for the objects in the undivided state, so that the region to be matched is not narrowed, and matching of the images of the dividable medical article is achieved with desirable accuracy. In addition, since the first and second display processing is performed on the images for display determined to contain the objects of the same type, matching results can easily be confirmed.

A method for generating an object detection dataset and a computer implemented object detection system are disclosed. The method comprises:

receiving a training image dataset comprising a plurality of images that include objects of interest, each image comprising pixel values corresponding to an imaged material generated by a penetrating imager;

generating a thresholded image for each of the plurality of images;

segmenting each thresholded image into images corresponding to objects;

creating a greyscale image per object from the segmented images corresponding to that object by, for each object, calculating an average pixel value for each pixel of the object from corresponding pixels of the object in the segmented images;

forming a greyscale image for the object from the averaged pixels;

storing the greyscale images in a data repository as an object detection dataset.

In some examples, it is disclosed a method for generating an image retrieval system configured to rank a plurality of images of cargo from a dataset of images, in response to a query corresponding to an image of cargo of interest generated using penetrating radiation. The method may involve obtaining a plurality of annotated training images including cargo, each of the training images being associated with an annotation indicating a type of the cargo in the training image, and training the image retrieval system by applying a deep learning algorithm to the obtained annotated training images. The training may involve applying, to the annotated training images, a feature extraction convolutional neural network, and applying an aggregated generalized mean pooling layer associated with image spatial information.

Described herein are systems for detecting a representation of an object in a radio frequency (RF) image. The system transmits one or more first RF signals toward an object, and receives one or more second RF signals, associated with the one or more transmitted RF signals, that have been reflected from the object. The system determines a plurality of first feature maps corresponding to a RF image associated with the one or more second RF signals. The system combines the plurality of first feature maps. The system further detects a representation of the object in the RF image based at least in part on the combined plurality of first feature maps.

Described herein are examples of evaluating electromagnetic energy reflection data of security scans. In embodiments, a method to evaluate electromagnetic energy reflection data determines whether electronic information of a security scan contains an anomaly, and identifies an anomaly location in the electronic information corresponding to the anomaly. The method determines a subset of the electronic information corresponding to the anomaly location, determines anomaly attributes using the subset of the electronic information, and evaluates the anomaly attributes using a database of reference items by comparing anomaly attributes to respective reference characteristics of reference items or identity information. When a comparison meets the respective match criterion for the given reference item, the method assigns to the anomaly the respective identifier as an anomaly identifier.