Security Checkpoint Image Processing for Concealed Electronics Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to accurately detect prohibited objects, such as electronic devices and hazardous materials, at security checkpoints, particularly when they are concealed within or masked by luggage, leading to inefficiencies and potential safety risks.
Innovation Solution
A system utilizing machine learning models to analyze images of luggage and identify the presence of electronic devices, triggering alarms based on their correct placement and, if concealed, further analyze the images for suspicious elements using additional algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image acquisition systems are used to detect prohibited objects, then the system structure is simple, but the detection accuracy is insufficient when objects are concealed within luggage
Solution Approach 1:
The system segments the detection process into multiple specialized machine learning models: a first ML model detects electronic devices and their locations, while a second ML model detects suspicious elements in specific regions. This segmentation allows each model to specialize in particular detection tasks, improving overall detection accuracy for concealed objects while managing system complexity through modular architecture
Solution Approach 2:
The system transitions from traditional single-alarm approaches to a multi-dimensional detection framework that processes images through multiple ML models with different detection focuses. The first dimension detects electronic devices, the second dimension detects suspicious elements, enabling comprehensive analysis of concealed objects that traditional single-dimension systems miss
2Measurement precision
If multiple machine learning models are deployed to detect both electronic devices and suspicious elements, then detection accuracy improves, but processing time increases
Solution Approach 1:
The first machine learning model performs preliminary detection of electronic devices and their locations before the second model analyzes suspicious elements. This preliminary action allows the system to pre-identify regions of interest, so the second model only needs to process specific areas rather than entire images, reducing overall processing time while maintaining high detection accuracy
Solution Approach 2:
The system applies partial action by having the second ML model process only specific sub-regions of the image where suspicious elements are likely to be located, rather than analyzing the entire image. This partial processing approach significantly reduces computation time while maintaining detection accuracy for concealed prohibited objects
3Reliability
If the system triggers alarms for all detected electronic devices, then security compliance is ensured, but false alarms increase when devices are correctly placed
Solution Approach 1:
The system applies local quality by differentiating detection and alarm logic based on the location of electronic devices. Devices detected within luggage trigger alarms (indicating potential concealment), while devices detected outside luggage do not trigger alarms (indicating proper placement). This location-based differentiation ensures security compliance while eliminating false alarms from correctly placed devices
Solution Approach 2:
The system creates a digital representation (copy) of the physical luggage and item locations through image processing and ML analysis. By working with this digital copy, the system can accurately determine spatial relationships between devices and luggage, enabling precise judgment of whether devices are concealed or properly placed, thus reducing false alarms while maintaining security
4Measurement precision
If the system processes entire images for suspicious element detection, then detection thoroughness is maintained, but processing efficiency decreases
Solution Approach 1:
The system extracts and processes only the relevant sub-regions of images where suspicious elements are likely to be located, rather than processing entire images. The first ML model identifies electronic device locations, and the system extracts these specific regions for second model analysis. This extraction approach maintains detection thoroughness for suspicious elements while significantly improving processing efficiency by reducing the amount of data to be analyzed
Data Source
Figure 1
Figure 2A~2B
Figure 3
AI summary
There are provided systems and methods comprising obtaining an image acquired by an acquisition device, using the image and a first machine learning model to determine whether (i) or (ii) is met in the image: (i) at least one electronic device of a first category is present within, below, or on luggage of a second category; (ii) at least one electronic device of the first category is present, wherein said at least one electronic device is not located within, below, or on luggage of the second category; responsive to a determination that (i) is met, triggering an alarm of a first type; responsive to a determination that (ii) is met, using at least part of the image and an algorithm different from the first machine learning model to determine whether a suspicious element is present in the at least part of the image.