https://github.com/make2explore/raspberrypi-object-detection
Real Time Object Detection on Raspberry Pi using TensorFlow Lite
https://github.com/make2explore/raspberrypi-object-detection
Last synced: 3 months ago
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Real Time Object Detection on Raspberry Pi using TensorFlow Lite
- Host: GitHub
- URL: https://github.com/make2explore/raspberrypi-object-detection
- Owner: make2explore
- Created: 2022-04-07T11:11:38.000Z (almost 4 years ago)
- Default Branch: main
- Last Pushed: 2022-04-07T14:43:51.000Z (almost 4 years ago)
- Last Synced: 2025-03-12T10:11:27.451Z (about 1 year ago)
- Language: C++
- Size: 22 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 1
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Metadata Files:
- Readme: README.md
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README
**[Project] Real Time Object Detection on Raspberry Pi using TensorFlow Lite SSD**
This application detects multiple objects in a scene. The most commonly used models are the SSD (Single Shot Detection) and YOLO (You Only Looks Once). The COCO SSD MobileNet v1 recognizes 80 different objects. It can detect up to ten objects in a single scene.
- A fast C++ implementation of TensorFlow Lite on a bare Raspberry Pi 4 64-bit OS.
- Once overclocked to 1925 MHz, the app runs a whopping 24 FPS! Without any hardware accelerator, just you and your Pi.
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**Prerequisites**
- Installation of OpenCV from source 🔗 [https://bit.ly/3xbB3Jk]
- Installation of Code::Blocks IDE in Raspberry pi OS
> $ sudo apt-get install codeblocks
- Tuneup your Raspberry Pi for Vision based projects - ▶️ [https://youtu.be/00c2GTpRaU8]
OR
- You can just download **SD image** of a Raspberry Pi 4 with **pre-installed frameworks and deep-learning examples**. This image is created by [Q-engineering](https://qengineering.eu). Find a complete working Raspberry Pi 4 dedicated to deep learning on following GitHub page link. Download the **zip file** from GDrive site, unzip and **flash the image** on a 16 GB SD-card, and enjoy!
 Find this example on Q-engineering's [SD-image](https://github.com/Qengineering/RPi-image)
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**Object Detection main code Citations** -
Source code -->
📎 Created by [Q-engineering](https://qengineering.eu)
📎 Referred by make2explore 2022/03/24
Source Code Credits ❤️ - Thank you - [Q-engineering](https://qengineering.eu)