cafe_management_system-raspi4-

cafe_management_system(raspi4)

https://github.com/taehuenkang/cafe_management_system-raspi4-

Science Score: 44.0%

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CITATION.cff file
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Scientific vocabulary similarity
Low similarity (6.4%) to scientific vocabulary

Last synced: 10 months ago · JSON representation ·

Repository

cafe_management_system(raspi4)

Basic Info

Host: GitHub
Owner: taehuenkang
License: agpl-3.0
Language: Python
Default Branch: main
Size: 28.8 MB

Statistics

Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Releases: 0

Created over 1 year ago · Last pushed 12 months ago

Metadata Files

Readme Contributing License Citation

☕ Cafe Management System – AI CCTV with YOLOv5 on Raspberry Pi

AI-enabled smart surveillance system designed for unmanned cafés.
It detects spills, lost items (phone, wallet), and displays real-time monitoring.

📌 Project Overview

📷 Real-time object detection using YOLOv5 (custom-trained)
🧪 Recognizes: coffee_cup, wallet, phone, spills
🖥️ Web dashboard via Flask to display live video & status
🍓 Hardware: Raspberry Pi 4, PiCamera2

🧰 Tech Stack

| Component | Tools / Frameworks | |---------------|------------------------------------------| | AI Model | YOLOv5 (PyTorch) | | Backend | Flask, OpenCV | | Device | Raspberry Pi 4, PiCamera2 | | Labeling | Roboflow, LabelImg | | Language | Python |

🧠 Key Features

👁️ Live object detection streamed via Flask (MJPEG)
🧼 Spill detection with size threshold alert
🔒 Lost item snapshot auto-save (every 10s)
📦 Lightweight enough to run on Pi (option to scale to Jetson)

🖼️ Model Info

| Metric | Value | |------------|------------| | mAP@0.5 | 97.2% | | F1 Score | 0.95 | | Precision | 99.6% | | Classes | 4 |

🔗 Links 🧠 YOLOv5 Training Config

🧪 Dataset & Label Format (YOLO txt)

📽️ Demo Video

🌐 Web Interface

Displays bounding boxes with class labels
Status panel for:
- Detected spills
- Saved snapshots with timestamps

🧪 How to Run

```bash git clone https://github.com/SoCafeManager/Management-of-unmanned-cafes.git cd cafe_project/ python3 app.py

Owner

Login: taehuenkang
Kind: user

Repositories: 1
Profile: https://github.com/taehuenkang

Citation (CITATION.cff)

cff-version: 1.2.0
preferred-citation:
  type: software
  message: If you use YOLOv5, please cite it as below.
  authors:
  - family-names: Jocher
    given-names: Glenn
    orcid: "https://orcid.org/0000-0001-5950-6979"
  title: "YOLOv5 by Ultralytics"
  version: 7.0
  doi: 10.5281/zenodo.3908559
  date-released: 2020-5-29
  license: AGPL-3.0
  url: "https://github.com/ultralytics/yolov5"

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