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Object Detection for Inventory Stocking — AI & Machine Learning project by YenkoDev

Object Detection for Inventory Stocking

Software as a Service

No

Category

AI & Machine Learning

Clients

Mapua University

Techstack

PythonYOLOv5PyTorchOpenCVFlaskRaspberry Pi

Purpose

Counting stock by hand is tedious and error-prone. This study applies YOLOv5 to automatically identify and count Cherry Mobile Aqua S9 and Flare S8 phones in a storage cabinet, keeping inventory records accurate without manual checks.

Description

A camera watches the storage cabinet while a YOLOv5 model, trained in Google Colab and run with PyTorch, detects and counts each phone model in the frame. A Flask web app streams the annotated live feed and current counts to the browser, and detections are logged to CSV for inventory records. The full pipeline runs on a Raspberry Pi with OpenCV handling capture and frame processing.

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