• Author: Alexandru Vrabii
• Email: alexandru.vrabii@stud.acs.upb.ro
• Master: AAC
• Academic year: 2023-2024

The idea for this project came from the desire to develop my bachelor project in a different direction. I wanted to try to develop an image recognition system based on ESP32, which is not exactly meant for image processing and analysis.The implementation of this project would lead to reduced expenses for developing an image recognition system. The ESP32-CAM board is much cheaper than a dedicated image processing board, such as the NVIDIA Jetson Nano.

Therefore, the ideal scenario would be to collect images using the ESP32-CAM, to train a machine learning model that would learn to recognize three types of objects. The ESP32-CAM recognizes the object and sends the information to a Firebase database. Using an ESP8266, I retrieve the information from Firebase and depending on the recognized object, I control an SG90 servo motor to simulate sorting the recognized objects.

During the development of the project, I encountered several difficulties, I understand why ESP32-CAM is not the most optimal solution for an Image Recognition project. However, I manage to solve some of them and I can show a MVP of the initial idea.

TODO :

• Demo
• Presentation slides

• AI Thinker ESP32-CAM - based on the ESP-32S module that integrates WIFI and Bluetooth
• ESP8266 - low-cost Wi-Fi microchip, with built-in TCP/IP networking software, and microcontroller capability
• OV2640 Camera - supports jpeg image output format
• SG90 - Tiny and lightweight with high output power.

TBD

TODO : insert a photo here

Software Arhitecture have three parts:

• Data acquisition and ML model training
• 
• Image Recognition and Firebase Population
• 
• Data acquisition from Firebase and decision making
• 

TBD

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The application logic is divided into * parts:

Hardware components documentation and datasheets

External Libraries

• Arduino

• Javascript/CSS

• Python

Litterature