Radar-Based Concealed Object Detection Using Convolutional Neural Networks
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Solution Overview
Problem
Current concealed object detection systems, such as metal detectors and X-ray scanners, are ineffective in detecting non-metallic objects and can be harmful, especially for children and pregnant women, and are cumbersome in scanning large numbers of people, while radar-based systems require short scan times to capture clear images of moving subjects.
Innovation Solution
A radar-based system using a sensor unit with transmitters and receivers to generate a 3D matrix of voxels, processed by a pre-processing unit to create convoluted slices, and analyzed by a convolutional neural network to detect specific concealed objects, reducing false alarms and providing real-time imaging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If radar-based systems are used to scan moving subjects, then scan time can be reduced, but image clarity deteriorates
Solution Approach 1:
The system performs preliminary actions by transmitting multiple radar signals at different frequencies and receiving reflections before processing. The pre-processing unit generates convoluted slices from raw complex image data, preparing the data structure in advance for the convolutional neural network to efficiently detect concealed objects during the scan
Solution Approach 2:
The radar system uses periodic transmission of electromagnetic signals at different frequencies to scan the subject. The system transmits signals continuously at multiple frequencies and processes the reflected signals periodically, allowing for both fast scanning and maintained image quality through frequency diversity
2Measurement precision
If conventional metal detectors are used, then detection of metallic objects is improved, but detection of non-metallic objects deteriorates
Solution Approach 1:
The radar-based detection system is designed to detect all types of concealed objects regardless of material composition. By using electromagnetic radiation in the radar frequency range, the system can detect metallic objects, non-metallic objects, liquids, and organic materials uniformly, replacing the material-specific detection of conventional metal detectors
Solution Approach 2:
The system changes the detection parameter from electrical conductivity (used by metal detectors) to electromagnetic reflection properties. By transmitting radar signals at specific frequencies and analyzing the reflected signals, the system can detect objects based on their dielectric properties and physical shape, enabling detection of non-conductive materials
3Measurement precision
If X-ray scanners are used for concealed object detection, then imaging capability is improved, but safety deteriorates
Solution Approach 1:
The system replaces the X-ray imaging mechanism with a radar-based electromagnetic detection mechanism. Instead of using ionizing radiation to create images, the system uses radar signals to detect concealed objects through their reflection properties, providing imaging capability without the harmful effects of X-rays
Solution Approach 2:
The system converts the potential harm of radiation exposure into a benefit by using non-ionizing radar frequencies that are safe for all subjects. The electromagnetic radiation used is at power levels and frequencies that do not pose health risks, while still providing effective concealed object detection
4Measurement precision
If full body scanners rotate around the subject, then complete surface exposure is improved, but scanning efficiency deteriorates
Solution Approach 1:
The system transitions from a spatial rotation approach (mechanical movement around the subject) to a frequency dimension approach. By transmitting signals at multiple frequencies and processing reflections in the frequency domain, the system achieves complete surface coverage without mechanical rotation, maintaining both completeness and efficiency
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient detection of specific concealed objects with reduced false alarms and fast scan times, suitable for use in security and production environments, while ensuring safety and accuracy in imaging moving subjects.
Implementation Method 1
transmitting a beam of electromagnetic radiations towards the target subject and receiving a beam of electromagnetic radiations reflected from the target subject
Data Source
AI summary
Systems and methods for scanning concealed surface and detecting concealed objects using a radar that transmits electromagnetic radiations towards a subject receives the reflected electromagnetic signals, a processing unit that receives raw complex image data from the radar unit and processes the data using a complex convolution neural network to detect concealed objects, a display unit that displays images representing the concealed object, a database that stores the processed data along with the raw complex image and the processed image data to train the processing unit to detect specific concealed objects, and a communicator that transmits notifications through a communication network.