An ESP32-CAM based object detection project using Edge Impulse, featuring real-time inference and live video streaming via a built-in web server.

Real-time object detection of microcontrollers and sensors (ESP32, Arduino, DS18B20, etc.) using ESP32-CAM and Edge Impulse. Ideal for smart inventory, embedded demos, and edge AI applications.

Object detection using ESP32-CAM and Edge Impulse. Detects Obi-Wan Kenobi and Batman LEGO figures

Real-time object detection on ESP32-CAM using Edge Impulse and YOLO, enabling lightweight AI inference at the edge.

Object detection using ESP32-CAM and Edge Impulse

Object detection using ESP32-CAM and Edge Impulse.

Edge-based object detection system using ESP32-CAM and Edge Impulse for industrial safety and educational applications.

Low-cost 6 DOF robotic arm using Arduino and ESP32-CAM with Edge Impulse based object detection.

Object Detection using ESP32-CAM and Edge Impulse is a TinyML project where an ESP32-CAM module performs real-time object detection using a deep learning model trained in Edge Impulse Studio. The model is deployed directly on the ESP32, enabling low-power, on-device computer vision without reliance on cloud services.

Real-time object detection using an ESP32-CAM and a custom model trained on Edge Impulse. Captures images, processes them on-device, and outputs detection results via serial or WiFi.

Real-time object detection system deployed on ESP32-CAM using Edge Impulse's FOMO (Faster Objects, More Objects) architecture. Capable of classifying and locating fruits with low latency (~10-15 FPS) on constrained hardware. Implementation - https://youtu.be/mR422r0TZhM

using edge impulse and self generated input data which is used to to train this object detection and classification model which is deployed on an esp32 cam board mounted on griper to calculate the distance beteen the object and the camera.

Real-time object detection on ESP32-CAM using Edge Impulse. Captures JPEG frames via the OV2640 camera, converts to RGB888, resizes/crops to model input size, and runs on-device ML inference. Supports bounding box detection, classification, and anomaly detection. Compatible with AI Thinker and ESP-EYE camera modules.