BAGCOUNT_AI
Automated Warehouse Bag Counting via Computer Vision
Custom-trained YOLO v8 model for real-time bag detection and counting in high-volume distribution centers. Achieves 99.2% accuracy with sub-100ms latency, eliminating manual counting errors.
The Problem
Manual inventory counting in warehouses consumed hours of labor and introduced systematic errors (3-5% miscounts). Existing automated solutions failed to achieve required accuracy in real-world lighting and stacking conditions.
Our Solution
Custom computer vision pipeline with transfer-learned YOLOv8 model trained on 45,000+ warehouse images. Edge deployment with TensorRT optimization for real-time inference. Integrated monitoring dashboard for verification.
Key Features
Outcomes & Impact
Achieved 99.2% counting accuracy (vs 95-97% manual)
Reduced truck verification time from 45min to 5min
Eliminated 100% of human counting errors
Saved $420K annually in labor costs
Enabled real-time inventory visibility
Video Demo for BAGCOUNT_AI
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