From "Human Eye Inspection" to "Human-Machine Collaboration": How AI Reshapes X-Ray Image Recognition in Security Screening
——An In-Depth Analysis of AI’s Technical Implementation and Practical Value in Security Equipment
In busy airport terminals, security inspectors stare intently at X-ray images of luggage on their screens. Complex overlapping items, varied concealed forms, and sustained high levels of concentration are daily challenges for traditional manual image inspection. However, with the deep integration of artificial intelligence technology, this scenario is undergoing a revolutionary transformation.
Recently, Xiamen Airport has fully upgraded its X-ray baggage image review system, introduced a "back-to-back dual-inspector" independent judgment system, and converted typical images into training materials. Behind this news lies the deep empowerment of AI technology: AI not only assists real-time image inspection but also plays a critical role in data accumulation and process optimization. So how exactly does AI "understand" X-ray images? What tangible changes can it bring to security screening?
I. How Does AI "Understand" X-Ray Images?
The core of AI-powered image inspection lies in the application of deep learning technology in the field of computer vision. For AI to interpret X-ray images, the following key steps are required:
Massive Data Training
AI models need large volumes of X-ray images for training, covering imaging features of various items—knives, firearms, liquids, explosives, electronic devices, and more—under different angles and occlusion conditions. For example, the OPIXray dataset developed by a research team at Beihang University contains 8,885 images of five types of knives, including folding knives, straight knives, and scissors, all manually annotated by professional security inspectors.
Deep Learning Algorithms
Current mainstream methods use Convolutional Neural Networks (CNN) for feature extraction. Using algorithms such as Mask R-CNN, researchers first select thousands of security images to label targets (e.g., liquids) in training samples, extract image features through various networks to generate candidate regions containing targets, and then train the model through three branch networks for classification, regression, and masking, ultimately yielding an optimized detection model.
Real-Time Detection Process
As luggage passes through an X-ray machine, the AI system completes multi-step processing within milliseconds: generating multi-angle/multi-energy images, automatically extracting image features (edges, textures, shapes, and colors corresponding to atomic numbers), bounding suspicious targets, and labeling categories (e.g., "knife", "suspected liquid"). The AI-annotated images are then sent to inspectors’ screens to assist judgment.
II. Four Core Changes Brought by AI
1. Improved Accuracy and Reduced Risk of Missed Detections
AI does not fatigue and maintains high alertness continuously, delivering a higher detection rate for small and concealed prohibited items. Practice at Kunming Airport provides strong evidence: over four months, the airport collected more than 30,000 images of typical prohibited items to build a dedicated image database, and collaborated with the Civil Aviation University of China and technology vendors to optimize algorithmic models, effectively improving hazardous item recognition accuracy in complex scenarios.
2. Enhanced Inspection Efficiency and Reduced Congestion
AI processes images rapidly, clearing non-threatening items quickly so inspectors can focus on reviewing suspicious images. The AI intelligent anti-miss detection system developed by Wuhai Airport, deployed with lightweight AI models and edge computing nodes, completes real-time analysis within 200 milliseconds, enabling precise baggage inspection and significantly reducing passenger screening time.
This efficiency gain underpins Xiamen Airport’s "back-to-back dual-inspector" model: AI acts as the "first inspector", automatically identifying and labeling suspicious images, which are then independently reviewed by two senior inspectors, forming a dual guarantee of "AI first pass + manual second pass".
3. Standardization and Knowledge Accumulation
The AI system records every inspection result and automatically collects images of typical prohibited items to form a standardized training library. The Li Yong Master Technician Studio at Xiamen Airport has converted typical reviewed images into practical "precision training materials" for daily training and skill improvement of security inspectors at all levels, alongside regular monthly blind testing to evaluate training effectiveness using real data.
4. Multi-Dimensional Data Fusion for Precise Suspicious Item Localization
Cutting-edge technology has enabled innovative applications of multi-dimensional data collection and fusion. The patented system developed by Xiamen Civil Aviation Keya Co., Ltd. uses X-ray, millimeter-wave, odor, and identity data collection modules to inspect passengers and luggage from multiple dimensions and establish a timeline for the security process. Critically, the system constructs 3D models of luggage to determine the 3D coordinates of items inside, and conducts secondary analysis of unidentifiable objects through a secondary analysis module for precise localization.
III. The Evolution of AI in Airport Security Screening
Xiamen Airport: Upgraded Back-to-Back Inspection and Image Review
Leveraging the Li Yong Master Technician Studio, Xiamen Airport has fully optimized and upgraded its X-ray baggage image review system. The upgrade transitions security from "single-point inspection" to "full-chain closed-loop management", with additional image review stations ensuring 100% coverage of carry-on, checked, and cargo mail images. A "back-to-back dual-inspector" system is implemented, where each scanned image is independently reviewed and cross-verified by two senior inspectors, achieving seamless integration of "front-end inspection + back-end review".
Kunming Airport: Pilot AI-Assisted Image Inspection System
The Security Screening Station at Kunming Airport has steadily piloted an AI intelligent assisted inspection system. Over four months, more than 30,000 images of typical prohibited items were collected to build a dedicated database. The system is now operational in three passenger security lanes. It provides real-time, objective inspection references and drives the shift from "human-dominated prevention" to deep integration of "human + technical prevention".
Wuhai Airport: AI Intelligent Anti-Miss Detection System
The AI intelligent anti-miss detection system developed by Wuhai Airport integrates multi-modal sensing, adaptive risk modeling, and edge computing to achieve three core breakthroughs: multi-sensor fusion for precise hazardous item identification; efficient parsing of raw X-ray machine data to support AI recognition; and edge computing enabling 200-millisecond real-time analysis and targeted re-inspection.
IV. Challenges and Future Trends
Current Challenges
Despite significant progress in AI inspection technology, challenges remain: recognizing heavily occluded and overlapping items remains difficult; training data must be updated promptly for newly emerging prohibited items; and high computing power requirements demand edge computing equipment. Researchers at Beihang University note that identifying occluded prohibited items in X-ray images remains a key challenge in computer vision, and their proposed De-Occlusion Attention Module (DOAM) represents an innovative attempt to address this issue.
Future Development Trends
Multi-Technology Integration
AI will be deeply integrated with millimeter-wave/terahertz body scanners and CT baggage scanners. A CAPA report indicates that the widespread adoption of Computed Tomography (CT) technology by 2026 will end the era of separate screening for liquids and laptops. These machines generate rotatable 3D images of luggage contents, with AI automatically marking explosives or prohibited items, cutting security time by half.
Security Screening Internet of Things
Interconnected equipment and cloud-based data aggregation will become mainstream. Kunming Airport has independently developed a "full life cycle management system for security equipment", creating dynamic "electronic health records" for its 270 security devices. Inspectors scan dedicated QR codes to upload real-time operational data, enabling closed-loop management from installation and acceptance to decommissioning.
Contactless Security Screening
Two major airports in Shanghai have streamlined domestic flight security, eliminating the need for passengers to remove laptops and power banks separately. CAPA projects that by 2030, traditional security checkpoints may disappear entirely, replaced by "walk-through channels" where passengers are scanned for threats while moving.
