One of the largest logistics and courier companies in the world
Increase customer satisfaction and reduce false damage shipment claims, by identifying damaged shipments at each transit point. The objectives were as follows:
• From a captured image of a parcel, predict if it is already damaged.
• Reduce false positives and thereby cut manual efforts and operational risk.
A deep learning-based solution was created to identify the damaged parcels from the captured images.
Achieved an accuracy of more than 90% in classifying the parcels as damaged or undamaged.
The model was tuned to predict 100% damaged shipments correctly.