北京道信德辰科技有限公司
AI Centralized Security Image Review System

System Overview

Revolutionizing the outdated model of assigning one on-site image reviewer per security checkpoint, this system transmits baggage images from all checkpoints across the station to a centralized back-end review room. The system intelligently assigns tasks, allowing idle reviewers to remotely review images in a unified manner, with AI conducting initial screening and human reviewers performing secondary verification. During peak hours, multiple workstations collaborate; during off-peak hours, manpower is reduced, cutting image review positions by over 40%.

Core Capabilities

  • Intelligent Task Scheduling: Real-time image transmission, automatic task assignment based on workstation availability, balancing workload.
  • AI Pre-screening: AI front-end screening reduces pressure on on-site reviewers and provides foundational data support for the centralized review model.
  • Remote Baggage Opening Instructions: One-click dispatch of suspicious baggage to front-end inspection stations, forming a complete closed loop of "AI detection — remote review — on-site re-inspection."
  • Fault Emergency Switchover: Automatic switch to backup workstations in case of equipment failure, ensuring business continuity.
  • Passenger-Baggage Association: Binding passenger face, ID, and baggage information to achieve one-to-one correspondence.

Core Value

  • New Security Inspection Workflow: "Front-end collection + back-end centralized review"
  • Full Closed-Loop Process: Intelligent detection for initial screening + manual secondary confirmation + baggage opening re-inspection linkage, building a complete security inspection chain.
  • Significant Manpower Reduction and Efficiency Enhancement: Reduces image review positions by over 40%, achieving "manpower reduction and efficiency enhancement" for security staff, balancing workload, and substantially lowering labor costs.
  • Overall Security Inspection Efficiency Improved by 50%: Suitable for multi-checkpoint scenarios in large transportation hubs.

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