Breaking Information Silos in Smart Metro Security: An Edge–Cloud Collaborative AIoT Framework With Humanoid Agents

Conventional metro security screening systems rely on isolated X-ray devices and human operators, resulting in coordination delays, inconsistent detection performance, and limited throughput under high passenger flow. To address these limitations, this article proposes an edge–cloud collaborative artificial intelligence of things (AIoT) framework that integrates X-ray inspection, robot-side visual perception, safety-score-based fusion, collaborative task allocation model (CTAM)-based task scheduling, and humanoid intervention into a closed-loop perception–decision–execution workflow. For robust carried-item detection in crowded and occluded checkpoint scenarios, an object-aware enhancement (OAE) framework is developed, in which keypoint-guided attention (KGA) uses human pose keypoints to enhance passenger–object interaction features, and a carrying-state classifier (CSC) refines physically inconsistent detections based on human–object carrying relationships. A safety-score-based multimodal fusion strategy further combines X-ray density cues and robot-side visual confidence into a unified safety-screening score for recheck and warning decisions. In addition, a CTAM is formulated to coordinate speech, gesture, locomotion, warning-light, and conveyor-control resources under concurrent security events. Experiments conducted in a controlled real-world metro-checkpoint setting show that the proposed OAE model achieves 46.9% AP, 69.4% AP₅₀, and 60.1% AR. The fusion strategy achieves an area under the curve (AUC) of 0.92. Under the tested controlled metro-checkpoint scenarios, the complete system reduces the average processing time by 46.1% and improves the estimated hourly throughput by 85.5% compared with manual screening.