Object Detection using ESP32-CAM and Edge Impulse

This advanced project implements a real-time edge machine learning system using an ESP32-CAM module to capture, process, and classify visual data locally without relying on cloud processing. The core of the system is the ESP32-CAM, which integrates a microcontroller with an onboard camera sensor to capture video frames of target objects like vegetables placed in its field of view. To program and transfer the compiled machine learning model to the ESP32-CAM, a USB to Serial Converter is temporarily wired to the board via a breadboard and jumper wires during the flashing process. Once deployed, the custom computer vision model—trained via Edge Impulse and integrated using the Arduino IDE and the EloquentEsp32cam library—analyzes the pixel data directly on the microcontroller's limited processing architecture. When the system detects a specific trained object, it evaluates the classification confidence locally. Optionally, builders can wire a 0.96-inch OLED display to the ESP32-CAM using I2C communication protocols to show real-time inference results, text outputs, or detection labels directly on a small screen alongside the live camera feed.