AI artificial intelligence detection system (optional)

AI artificial intelligence detection system (optional)

AI (Artificial Intelligence), recognizes over 20 types of objects: guns, freworks, spray cans, knives, axes, scissors, lighter fuid, nail polish, botled liquids, glass botles, thermos botles, plastc botles, handcufs, nightstcks, brass knuckles, lighters, tools, electronic products, mobile phones, powerbanks, laptops, umbrellas and high density object), etc.

Overview of AI (Artificial Intelligence) Security Inspection Machines

AI security inspection machine is an intelligent security inspection device that integrates artificial intelligence technology. It mainly uses deep learning algorithms to analyze X-ray images or other sensor data in real time, automatically identify prohibited items (such as knives, explosives, drugs) in luggage, packages or human bodies, and achieve efficient and accurate security screening. Its core lies in upgrading traditional operations that rely on manual experience to automated and adaptive detection systems, significantly improving security inspection efficiency and reliability. The following analysis will focus on core technologies, functional advantages, application scenarios, and actual results.

I. Core Technology Architecture

Deep learning model driven: using AI architectures such as Convolutional Neural Networks (CNN) and YOLO series models (such as YOLOv8 or YOLOv13), training on massive X-ray image data to achieve multi-level extraction and classification of image features. For example, the system automatically identifies the location and category of items such as cutting tools and liquid explosives, and marks alarms in real-time. ‌

Multimodal sensing fusion: Combining technologies such as X-ray, millimeter wave, and infrared sensing to comprehensively evaluate risks from dimensions such as morphology, density, and material. The millimeter wave security scanner can capture the reflected signals on the skin surface, generate millimeter level resolution 360 ° imaging, and penetrate flammable and explosive materials that cannot be detected by traditional equipment. ‌

Adaptive learning capability: The system supports online software upgrades and optimizes models through continuous learning of new prohibited item features without the need for hardware modifications. For example, algorithms can be dynamically adjusted to meet the sensitivity requirements of different security check scenarios. ‌

II. Functional advantages compared to traditional security checks

Ability dimension: Traditional security inspection machine, AI security inspection machine

The accuracy of detection depends on the operator's experience, with a false detection rate>1% and an automatic recognition false detection rate ≤ 0.5%. It supports low probability high-risk item warning

Processing efficiency is slow due to manual image judgment, which can easily lead to queue backlog and real-time analysis, resulting in a 40% increase in travel speed (such as an airport processing over 100000 pieces of luggage per day)

Human dependence requires high-intensity manual monitoring to reduce 80% of repetitive labor and support 24/7 automated operations

Privacy and security devices have radiation risks. Millimeter wave radiation is only one thousandth of mobile phone signals, and privacy protection technology blurs sensitive areas

III. Key application scenarios

Civil Aviation and Transportation Hub

Deploying AI security screening machines at airports, integrating 3D CT scanning and automatic alarm, solves the problem of traditional 2D image obstruction, and increases the explosive detection rate to 98.5%. ‌

Logistics and express delivery

The Cainiao Smart Security Inspection System, through the linkage of the Internet of Things and big data, matches order information and X-ray images in real-time on high-speed conveyor belts (2 meters per second), intercepting over one million dangerous goods annually. ‌

Public Safety and Customs

The AI millimeter wave security scanner is applied in 23 customs ports at home and abroad, completing human body scanning within 2 seconds and accurately identifying corrosive and smuggled goods. ‌

IV. Actual results and case studies

In the field of civil aviation, the intelligent mapping system developed by Beijing Hangxing, based on the CNN model, has improved the accuracy of prohibited item recognition by 40% and continuously optimized its performance through online learning. ‌

In the field of logistics, Cainiao's solution reduces the accumulation of goods during peak periods, compresses the detection time of a single item to 3.2 seconds, and achieves an accuracy rate of intermediate security personnel in image judgment. ‌

Comprehensive indicator improvement: Data from a certain airport shows that AI security screening machines have increased the detection rate of explosives from 72% to 98.5%, and the false positive re inspection rate has decreased from 23% to 4.1%. ‌

The evolution direction of AI security screening machines includes integration with digital twin technology, enhanced algorithm robustness, etc., and is gradually becoming a core component of smart city security networks.