Vision-Based Damage Detection for Air Cargo Operations
Vision-Based Damage Detection for Air Cargo Operations
Business Overview
The client is a leading air cargo and ground handling services provider, managing high volumes of baggage and freight across major international airports. With millions of cargo units processed annually, ensuring the physical integrity of shipments is critical to their operations.
They sought to modernize their quality assurance processes to minimize liability claims and enhance the transparency of their handling operations.
Challenge
In the fast-paced environment of air cargo logistics, manual inspection of every
piece of luggage and cargo is labor-intensive and prone to human error. The client
faced several operational bottlenecks:
- Undetected Damage: Minor dents, tears, or seal breakages were often missed
during rapid manual checks, leading to disputes. - Operational Delays: Stopping conveyor belts for manual inspection reduced
throughput and delayed flight turnarounds. - Lack of Digital Audit Trail: Difficult to pinpoint exactly where and when
damage occurred for liability claims.
Solution
NeST Digital implemented a Vision-Based Damage Detection System powered by Edge AI and Computer Vision. The solution was designed to operate seamlessly within the existing conveyor infrastructure without disrupting the flow of cargo.
- Multi-Angle Imaging
High-speed industrial cameras installed at key checkpoints to capture 360-degree views of moving cargo. - AI Defect Recognition
Custom Deep Learning model trained to detect crushed corners, tears, broken seals, and dents with high precision. - Real-Time Edge Processing
Processing performed at the edge for low latency, enabling immediate flagging without slowing operations. - Automated Alerting
Automatically tags damaged images with timestamps and location data for centralized audit management.
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