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X-Ray Security Inspection Machine Industry Development Trend

X-Ray Security Inspection Machine Industry Development Trend

X-ray security inspection machine industry development trend

First, the speed of X-ray security scanning processing software continues to improve. The scanning software of security inspection machines presents a variety of development trends according to different caliber specifications. For example, small X-ray security inspection opportunities are getting smaller and smaller to meet the needs of post office boxes; while large X-ray security inspection machines may be larger and larger, or ADC scanning speed is faster, or more intelligent, or X-ray source lifespan longer. This trend has laid the foundation for the expanded application of security X-ray machines.

Second, integrate deeply into various application systems. The high-end X-ray security inspection machine is not a toy, it must undertake some high-standard security inspection tasks; it will not be alone, and will be more and more integrated into various security application systems. The higher the degree of integration with the application system, the greater the role of the X-ray security inspection machine. For example, in the future, X-ray security inspection machines must perform tasks together with unmanned security inspection machines, each undertaking their own tasks, while cooperating with each other to complement each other. This depends not only on the continuous strengthening of the X-ray security inspection machine's ability to distinguish dangerous objects, but also on the increasing degree of intelligence, information transmission, and artificial intelligence decision-making of the system.

In-depth integration of intelligent decision-making for industrial application systems

Real-time automatic identification of hazardous chemicals and contraband in X-ray pictures, sound, light and electrical alarms, improve the efficiency of operator's image judgment.

The CNN convolutional neural network algorithm is used for feature extraction, reasoning and classification storage of X-ray images, and a data model of dangerous contraband and dangerous chemicals is established through deep learning to realize real-time identification of the information content of X-ray scanned images.

1. Labeling of contraband: mark the location and category of contraband in the perspective image of the package, and check the quantity of contraband.

2. Identification and alarm: sound, light and electric alarm function for contraband and dangerous chemicals.

3. Label the names of contraband: such as suspected guns, controlled knives, liquid containers, batteries, hazardous chemicals, lighters, etc.

4. Conveyor automatic sorting control: realize sorting action control for boxes and packages that have identified hazardous chemicals and contraband.

5. Autonomous deep learning and memory function: Through machine vision analysis and decoding of material density information, the accuracy of X-ray image recognition is improved.

6. Ultra-perceptual recognition function: learn and compare through real-time scanning image data and the data model library of dangerous chemicals and contraband, automatic alarm when the similarity exceeds the threshold, and verify the theory through examples. When multiple security inspection machines are working in a cluster, each The security inspection machine generates recognition model data in the cloud, and then transmits it back to the local security inspection machine's local recognition model database, so that the local security inspection machine recognition model database continues to evolve, improving the efficiency and accuracy of image recognition.

7. Edge computing technology for security inspection scenes: artificial intelligence working in security inspection machine clusters, through historical recognition of model data and prediction of X-ray image data to be recognized in this area, to coordinate the image recognition efficiency of each local security inspection machine, and to realize all security inspections in the area accuracy of image recognition.