Introduction
The Maxvision AI Analysis Terminal leverages deep learning–based algorithms to automatically analyze and interpret X-ray security images. It can identify over a hundred categories of prohibited items, including corrosives, explosives, infectious substances, spontaneously combustible materials, flammable solids and liquids, water-reactive substances, organic peroxides, oxidizers, toxic substances, poisonous gases, and contraband such as banned publications, pirated media, and counterfeit branded goods (detection of certain items may require specific hardware configurations). The system offers high accuracy, operational stability, intelligent data processing, and powerful analytics capabilities—enabling efficient and reliable threat detection in modern security screening environments.

Features

• Streamlined Security Inspection
• Intelligent and Trend-Driven Management
• Digitalized Monitoring and Alarm Systems
• Modular and Network-Enabled Devices

Introduction
Maxvision’s AI Video Analytics Server delivers services through two core approaches: an intuitive image retrieval interface and a comprehensive algorithm API. The image retrieval interface offers a user-friendly experience designed for essential tasks such as object detection, feature extraction, and target comparison. Meanwhile, the algorithm API provides a broader range of advanced capabilities—including object detection, attribute recognition, feature extraction, image archiving, as well as both 1:1 and 1:N matching—ensuring high algorithmic accuracy while supporting high-concurrency demands.
Built on a microservices and distributed architecture, the server is highly scalable—supporting both standalone deployment and large-scale distributed deployment. This architecture maximizes computational efficiency, enabling fast, accurate, and stable analysis of high-volume image data from thousands of surveillance streams. It is ideally suited for use in business parks, retail complexes, airports, border checkpoints, and other scenarios requiring intelligent image recognition and matching.

Features

• Compatible with Mainstream Hardware and Software
• Multiple AI Acceleration Options
• Broad Model Compatibility
• High-Performance, High-Accuracy Algorithms
• Flexible Deployment Modes

Introduction
The Maxvision Image Retrieval Server delivers AI-powered image analysis services through two flexible modes: a visual retrieval interface and a developer-oriented algorithm API. The visual interface provides an intuitive user experience for essential tasks such as object detection, feature extraction, and similarity matching.
The algorithm API offers a more comprehensive suite of capabilities—including object detection, attribute recognition, feature extraction, image indexing, as well as both 1:1 and 1:N comparison—ensuring high algorithmic accuracy while supporting large-scale concurrent requests.
Built on a microservices-based distributed architecture, the server is designed for both standalone and scalable deployments. It maximizes computing efficiency to deliver fast, accurate, and stable analysis of massive surveillance image streams—making it an ideal solution for complex environments such as industrial parks, retail hubs, airports, and border control facilities where intelligent visual recognition and matching are critical.

Features

• Compatible with Mainstream Hardware and Software
• Multiple AI Acceleration Options
• Broad Model Compatibility
• High-Performance, High-Accuracy Algorithms
• Flexible Deployment Modes

Introduction
The Vehicle Recognition and Detection Algorithms class encompasses a series of advanced intelligent algorithms based on deep learning, computer vision, and image processing technologies. These algorithms are widely applied in fields such as traffic management, logistics monitoring, and intelligent security.
They enable the automated identification and analysis of vehicles and related identifiers, covering functionalities such as license plate recognition, vehicle attribute identification, traffic flow analysis, and parking status detection. Capable of efficiently and accurately processing complex image and video data in various environmental conditions, these algorithms significantly enhance automation and intelligent decision-making across industries like traffic management, logistics, and security.

Introduction
Powered by deep learning and video analytics, the Traffic Incident Detection Algorithm suite accurately identifies a wide range of abnormal driving behaviors and risk events—including illegal parking, wrong-way driving, congestion, fatigue driving, pedestrian or animal intrusion, object dropping, emergency lane occupation, and lane departure.
The system also incorporates intelligent driver attribute recognition, enabling comprehensive situational awareness of traffic participants. Designed for complex environments such as urban roads, highways, and transportation hubs, it enhances traffic management with greater automation, precision, and responsiveness.

Introduction
Personnel Behavior Analysis Algorithms leverage deep learning and computer vision technologies to intelligently identify and analyze specific human behaviors. These algorithms provide real-time monitoring of abnormal behaviors in environments such as public spaces, commercial areas, and transportation hubs, enabling rapid response and security alerts.
By accurately identifying behaviors such as loitering, boundary intrusion, abandoned items, crowd gathering, running, falling, fighting, and climbing, these algorithms enhance security and risk prevention capabilities. They are widely used in security surveillance, smart buildings, and public safety applications.

Introduction
Operational Safety Monitoring Algorithms leverage advanced computer vision and deep learning technologies, designed specifically to safeguard personnel in high-risk work environments. These algorithms provide real-time monitoring and automatic recognition of worker behaviors and protective gear, ensuring compliance with safety regulations and promptly identifying potential hazards.
Widely used in construction, mining, and industrial sectors, they significantly reduce the risk of safety incidents and enhance on-site safety management efficiency.

Introduction
Personnel Recognition Algorithms combine biometric recognition technology and data analysis methods to accurately identify and analyze individuals' identities, behaviors, and crowd density. By monitoring real-time biometric features, behavioral patterns, and movement, these algorithms are widely applied in security, public safety, and smart city sectors, enhancing security management, personnel flow monitoring, and emergency response capabilities.