All Technologies Used
Motivation
To automate the manual process of counting 200–300 pallets daily in a warehouse environment with limited connectivity. The goal was to deliver a highly accurate (≥95%) and autonomous system running offline on an edge device to reduce human error and save time.
Main Challenges
The warehouse had no internet connectivity, requiring a locally deployed ML solution. Azati optimized a deep learning model to run entirely offline on NVIDIA Jetson, using CUDA, cuDNN, and TensorRT for efficient GPU acceleration.
Pallets were often stacked in irregular or overlapping patterns. Azati refined the detection system using YOLOv8 and DeepSORT, supported by OpenCV preprocessing, to reliably detect and track pallets despite cluttered visuals.
Key Features
- Offline Vision-Based Counting: Fully autonomous pallet counting without any internet connection, using an edge computing device.
- High Accuracy Detection: Achieved 95% accuracy in detecting and counting pallets from real-time video streams.
- Real-Time Object Tracking: Integrated DeepSORT to track detected pallets even under overlapping or shifting conditions.
- Scalable Architecture: The model and system design allow future updates and scaling to different warehouse layouts or camera setups.
Our Approach
Project Impact
The automated pallet counting system streamlined warehouse operations by eliminating manual tasks, reducing human error, and achieving high-accuracy performance. The offline capability ensured full autonomy in low-connectivity environments, and the solution is scalable for future expansion or enhancement.