Context

Developed for Petrovietnam Ca Mau Fertilizer Corporation (PVCFC) during an onsite internship, this system automates inventory management in agricultural supply chains. The goal was to build a computer vision pipeline capable of detecting, tracking, and classifying fertilizer bags on moving conveyor belts under challenging industrial conditions.

Approach

Trained a custom YOLOv8 model for high-speed object detection and classification. Integrated DeepSort for multi-object tracking to prevent double-counting as bags pass the camera sensor. The pipeline is written in Python using OpenCV for frame-by-frame processing and video ingestion, optimized to run with low latency on edge computing devices inside the manufacturing environment.

Outcome

~92% Accuracy · Encouragement Award, CTU.

Reflection

Industrial AI is less about benchmark data and more about handling noise, dust, lighting changes, and camera vibration. Tuning YOLOv8 to achieve ~92% accuracy under variable physical conditions was a masterclass in robust dataset curation and real-time inference optimization.