How to choose the right X-ray security screening machine?

At security checkpoints in airports and high-speed rail stations, an X-ray security scanner acts as an "invisible guardian" of safety, and its core capability lies in the clear, detailed images it produces. You might wonder why security personnel can quickly identify prohibited items on the screen? The answer lies in the clarity and color accuracy of the X-ray imaging, and its deep integration with AI recognition.

The imaging effect is not simply a "visual presentation," but rather the technological foundation for material identification, risk assessment, and intelligent early warning. From the X-ray images of luggage, we can directly see the gear meshing structure of a folding bicycle, the metal frame wiring of a suitcase, and the internal component arrangement of small electrical appliances. This refined imaging performance is backed by the deep coupling of imaging clarity, color reproduction, and AI recognition algorithms, and is also key to the transformation of modern security technology from "manual screening" to "intelligent prevention and control."

01. Imaging Resolution: The Physical Basis of Security Recognition

High-definition imaging leaves no place for prohibited items to hide. The image clarity of a security X-ray machine is essentially determined by both spatial resolution and density resolution. Spatial resolution determines the smallest object size that can be resolved in the image. In actual security scenarios, this indicator directly relates to whether tiny prohibited items, such as miniature knives hidden in luggage compartments or disassembled explosive device components, can be identified; density resolution can capture the differences in atomic numbers of different substances, allowing even metals and plastics of the same volume to be distinguished by differences in grayscale and detailed textures.

From the actual imaging results, the spokes and screws of a folding bicycle, which are micron-sized components, can be clearly distinguished, and the fabric texture of the suitcase and the welding points of the metal pull rod form a sharp contrast, fully demonstrating the effect of high-resolution imaging. If the resolution is insufficient, it will not only make tiny dangerous goods difficult to detect visually, but also lead to the loss of structural features of the items, creating information blind spots for manual analysis and AI recognition.

From the security X-ray image, it can be clearly observed that the frame texture, gear structure, and even the fine wiring arrangement of the folding bicycle inside the suitcase are clearly visible. This pixel-level imaging clarity breaks through the visual barriers of item packaging. Even prohibited items such as knives and batteries hidden in luggage compartments or between parts will be revealed in detail in the high-definition images. If the image is blurry, tiny dangerous items could be missed, but high-definition imaging directly eliminates this security risk, allowing security personnel to make more accurate judgments about the internal structure of items.

02 Color Reproduction: Digital Encoding of Material Properties

True colors provide materials with "identity tags." The color imaging of security X-ray machines is not merely for visual enhancement, but is based on a digital encoding system of the X-ray attenuation characteristics of materials. Substances with different atomic numbers have different abilities to absorb X-rays. The equipment uses algorithms to convert the attenuation coefficient into color signals: organic materials composed of light elements (such as fabrics and food) appear in orange-yellow tones, plastics and glass with medium atomic numbers show green, and metals with high atomic numbers (such as steel and copper) appear as dark blue to blue-black. The accuracy of this color reproduction is the core basis for rapid material classification. For example, in luggage images, blue metal components and orange liquid containers create a color contrast, allowing security personnel to quickly identify high-risk materials based on color characteristics; while deviations in color reproduction can directly lead to material misidentification—such as identifying metal parts as plastic, or classifying flammable and explosive organic liquids as ordinary fabrics, thereby causing security loopholes.

The imaging of security X-ray machines is not a monotonous black and white, but uses colors such as green, blue, and orange to distinguish different materials: metal components show a deep blue, plastics and fabrics show green tones, and organic materials are mainly orange. This high-fidelity color system is equivalent to giving each material an "identity tag," allowing security personnel to quickly identify the properties of items based on color characteristics—for example, blue metal components and orange liquid containers—without repeated confirmation, significantly improving security efficiency. 03. Imaging Quality and High-Definition Imaging + AI Create an "Intelligent Brain" for Security Screening

The application of AI in security screening is essentially based on pattern recognition of image features. High-quality imaging is the prerequisite for the effective operation of the algorithm, forming a symbiotic relationship between "data and algorithm":

Effectiveness of Feature Extraction:

AI algorithms need to extract feature values such as the outline, texture, and color of objects from images. High-definition imaging provides richer edge features and detailed textures, allowing the algorithm to accurately learn the structural characteristics of prohibited items (such as the trigger structure of firearms and the blade contour of knives); the accuracy of color reproduction provides the algorithm with stable material feature labels, reducing feature mismatches caused by color deviations.

Robustness of Model Training:

A large amount of high-definition, color-accurate imaging data allows the AI model to learn a more comprehensive distribution of object features, improving the ability to identify irregularly shaped and disassembled prohibited items. Conversely, blurry and color-distorted images will cause the model to have "cognitive biases," leading to increased rates of missed detections and false positives.

Response Speed of Real-Time Warning:

High-definition imaging reduces the complexity of feature processing for the algorithm, allowing AI to complete feature comparison and risk marking in milliseconds. Taking baggage security screening as an example, the algorithm can quickly locate abnormal metal components in bicycle parts from clear images and match them with the prohibited items database, achieving real-time risk warnings.

If high-definition imaging and true color are the "eyes" of security screening, then AI recognition is the "intelligent brain," and the联动 (interaction/collaboration) between the two is the core competitiveness of modern security screening. The training and application of AI recognition algorithms completely depend on high-definition, color-accurate X-ray images: only when the imaging is clear enough can AI accurately learn the outline and structural features of objects; only when the color reproduction is accurate can AI classify and identify objects based on the color labels of the materials.

Based on high-definition images, AI can compare against a massive database of prohibited items in milliseconds, automatically marking dangerous items such as knives, firearms, and flammable and explosive materials. This not only reduces the visual fatigue of security personnel but also avoids omissions in manual identification, upgrading security screening from "manual screening" to "intelligent warning." From a technical perspective, the clarity and color reproduction of security X-ray imaging are the key links to connecting "physical detection - digital analysis - intelligent decision-making." With the iteration of imaging technology and the optimization of AI algorithms, security screening systems are gradually achieving a leap from "seeing objects to understanding risks," and all of this is built upon every high-definition, precise X-ray image.

04. Simple Guide to X-ray Machine Imaging Quality Testing

I. Clarity Testing: Observing Details and Distinguishing Minute Features

Standard Reference Object Test:

Place objects with fine textures (such as metal parts with screws, woven fabrics) into the security scanner and observe whether the screw threads and fabric weave patterns are clearly discernible in the image. If the details are blurry or the edges are indistinct, it indicates insufficient spatial resolution.

Density Difference Test:

Stack a metal sheet and a plastic sheet of the same size and check whether the grayscale/texture differences between the two are obvious in the image. If they cannot be distinguished, it means the density resolution is not up to standard, which can easily lead to material misidentification.

Actual Item Verification:

Using complex items such as folding bicycles and suitcases as samples, check whether the welding points of the bicycle frame and the interlayer lines of the luggage are completely displayed in the image. No loss of detail indicates a qualified result.

II. Color Reproduction Testing: Checking Materials and Color Matching

Material Color Matching:

Prepare three types of objects: metal (iron nail), organic matter (bread), and plastic (water cup). After imaging, check whether the colors conform to the industry coding rules of "metal blue, organic orange, and plastic green." If there is color confusion (such as metal appearing green), the color reproduction is not up to standard.

Color Layer Detection:

Test with different metals (copper wire, iron wire) and observe whether the shades of blue in the image are distinguishable; test with fabrics of different densities (cotton cloth, synthetic fiber cloth) and check whether there are gradient differences in orange. Lack of layering indicates insufficient color dynamic range.

III. AI Adaptability Testing: Checking Image Quality and Distortion

Signal-to-Noise Ratio Testing:

When no objects are passing through, observe whether the image background has no obvious noise or snow patterns; when there are objects, check whether the edges of the objects are clearly defined against the background. Excessive noise will affect AI feature extraction. Geometric Distortion Detection:

Place a square suitcase directly in front of the security scanner entrance. After imaging, check if the suitcase's outline is a standard rectangle, with no stretching or distortion of the edges and corners. If so, the geometric distortion rate meets the standard; if deformation occurs, it will lead to misjudgment of the object's shape by the AI.

IV. Quick Judgment Principles

If the image meets the four core points of "details are discernible, colors are accurate, image quality is clean, and shape is undistorted," then the imaging quality of the current security X-ray machine meets the security inspection requirements; if any dimension is substandard, contact technical personnel immediately to calibrate the equipment.  We recommend ANDVN Guangdong Andun brand. Guangdong Andun has 15 years of R&D and manufacturing experience, is one of the top 10 security inspection brands, has 19 offices worldwide, over 100 intellectual property rights, over 100 honors and qualifications, and has been a national high-tech enterprise for 10 consecutive years. It is network-enabled, customizable, and available for OEM.  "Andun Brand, Trustworthy."

05. Core Advantages of X-ray Security Scanner Imaging Effects

Ultra-high definition, no details missed: The imaging can clearly show the internal structure of objects (such as the parts, metal frame, and wiring of a folded bicycle inside a suitcase). Even small parts and fine mechanical structures can be accurately identified, eliminating visual blind spots where prohibited items can be hidden.

Strong color layering and differentiation: Different materials are presented in differentiated colors such as green, blue, and orange, allowing for quick differentiation of material properties such as metal, plastic, and fabric. Security personnel can quickly determine the type of item through color characteristics, improving identification efficiency.

Excellent penetration: It can penetrate outer packaging such as suitcases and cardboard boxes, directly displaying the complete form and placement of internal items. Preliminary inspection can be completed without opening the box, significantly reducing the workload and time cost of manual unpacking.

High image fidelity: It can accurately restore the actual size, outline, and assembly relationship of objects (such as the frame proportion and part connection method of a bicycle), avoiding misjudgments caused by image deformation and improving the accuracy of security inspection results.