Manufacturing & Supply ChainAutomatic Box Counting & Anomaly Detection Using Computer Vision
The system leverages deep learning–based vision models to count physical products, detect anomalies, and operate reliably in fast-moving, real-world production environments.
Tech Stack
Python, PyTorch, YOLOv5, OpenCV, Deep Learning Models
Project Type
Production Automation Implementation
Service Type
Computer Vision & AI Automation
Industry
Manufacturing
About Client & Project
The client is a manufacturing company operating high-volume production lines where accuracy, speed, and quality control are critical. The organization focuses on operational excellence and continuous improvement, with a strong emphasis on automation, efficiency, and defect prevention across its manufacturing processes.
The Key Features We Integrated
Automated Box & Product Counting
Deep learning–based object detection and tracking accurately count individual products as they move across defined conveyor zones, eliminating double-counting and missed detections.
Real-Time Anomaly & Defect Detection
The system identifies damaged boxes, abnormal shapes, size deviations, misaligned packaging, and foreign objects in real time.
Intelligent Object Tracking
Each product is assigned a persistent ID across frames, ensuring precise tracking even with overlapping or closely spaced items.
Robust Image Preprocessing
Advanced lighting correction, noise reduction, and motion-aware filtering ensure stable performance in industrial conditions.
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