A computer vision framework for proactive anomaly detection and risk reduction in airport baggage logistics
· Source: nature.com
In short
This study proposes an advanced AI-driven computer vision system using the YOLOv8 algorithm and GPT-4 Vision to detect baggage accessories (bags, handles, straps) and damages (cracks) in airport baggage handling. The model demonstrates high precision and recall for bags and handles, while straps and damage detection show room for improvement. The framework achieves real-time processing speeds compatible with conveyor operations and offers promising economic and operational benefits for airport baggage logistics.
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In the issue of week 44, 2025: Agentic AI Drives Unmatched Productivity and Resilience in Supply Chains
Tags: baggage, detection, computer vision, deep learning, YOLOv8, logistics