Detecting Objects with the ESP32-CAM

The ESP32-CAM is a powerful, low-cost microcontroller with a built-in camera module, capable of capturing images and streaming live video. By leveraging its processing capabilities and integrating external machine learning libraries or frameworks, you can enable object detection directly on the ESP32-CAM. This tutorial will guide you through setting up basic object detection with the ESP32-CAM using pre-trained models.

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What You Will Need

1. ESP32-CAM Module
2. FTDI Programmer (USB-to-Serial adapter)
3. Jumper Wires
4. Breadboard (optional)
5. A computer with the Arduino IDE installed
6. Edge Impulse or TensorFlow Lite for object detection models

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Step 1: Setting Up the Arduino IDE

1. Install the ESP32 Board Package

1. Open the Arduino IDE.

2. Go to File > Preferences.

3. In the "Additional Board Manager URLs" field, add:

   https://dl.espressif.com/dl/package_esp32_index.json
   

4. Click OK.

5. Go to Tools > Board > Boards Manager.

6. Search for "ESP32" and install the package by Espressif Systems.

2. Install Required Libraries

• Install the "ESP32 Camera" library.
• For object detection models, install "TensorFlow Lite Micro" (or similar libraries).

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Step 2: Wiring the ESP32-CAM

ESP32-CAM Pin	FTDI Programmer Pin
GND	GND
5V	VCC (5V)
U0R	TX
U0T	RX
GND	GND (IO0 to GND for flashing)

Important: Connect the IO0 pin to GND to put the ESP32-CAM into flash mode.

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Step 3: Uploading a Basic Object Detection Sketch

1. Select the Board and Port

1. Go to Tools > Board and select AI-Thinker ESP32-CAM.
2. Under Tools, select the correct COM Port for your FTDI programmer.

2. Preparing the Code

Use a basic example to load a pre-trained object detection model (e.g., TensorFlow Lite model). Below is a sample snippet to integrate object detection:

#include <esp_camera.h>
#include <TensorFlowLite_ESP32.h>

// Replace with your specific model and labels
#include "model.h"
#include "labels.h"

void setup() {
  Serial.begin(115200);

  // Initialize the camera
  camera_config_t config;
  config.ledc_channel = LEDC_CHANNEL_0;
  config.ledc_timer = LEDC_TIMER_0;
  config.pin_d0 = Y2_GPIO_NUM;
  config.pin_d1 = Y3_GPIO_NUM;
  config.pin_d2 = Y4_GPIO_NUM;
  config.pin_d3 = Y5_GPIO_NUM;
  config.pin_d4 = Y6_GPIO_NUM;
  config.pin_d5 = Y7_GPIO_NUM;
  config.pin_d6 = Y8_GPIO_NUM;
  config.pin_d7 = Y9_GPIO_NUM;
  config.pin_xclk = XCLK_GPIO_NUM;
  config.pin_pclk = PCLK_GPIO_NUM;
  config.pin_vsync = VSYNC_GPIO_NUM;
  config.pin_href = HREF_GPIO_NUM;
  config.pin_sscb_sda = SIOD_GPIO_NUM;
  config.pin_sscb_scl = SIOC_GPIO_NUM;
  config.pin_pwdn = PWDN_GPIO_NUM;
  config.pin_reset = RESET_GPIO_NUM;
  config.xclk_freq_hz = 20000000;
  config.pixel_format = PIXFORMAT_JPEG;

  if (!esp_camera_init(&config)) {
    Serial.println("Camera initialized successfully!");
  } else {
    Serial.println("Camera initialization failed!");
    while (1);
  }

  // Load the TensorFlow Lite model
  TfLiteStatus model_status = tflite::InitModel(model);
  if (model_status != kTfLiteOk) {
    Serial.println("Failed to initialize the model");
    while (1);
  }
}

void loop() {
  // Capture a frame
  camera_fb_t *frame = esp_camera_fb_get();
  if (!frame) {
    Serial.println("Camera capture failed");
    return;
  }

  // Run object detection
  tflite::RunInference(frame->buf, frame->len);

  // Display detected objects
  for (int i = 0; i < NUM_CLASSES; i++) {
    float confidence = tflite::GetConfidence(i);
    if (confidence > 0.5) {
      Serial.print("Detected: ");
      Serial.print(labels[i]);
      Serial.print(" with confidence: ");
      Serial.println(confidence);
    }
  }

  esp_camera_fb_return(frame); // Free the frame buffer
  delay(200);
}

3. Upload the Code

1. Press the Reset button on the ESP32-CAM while IO0 is connected to GND.
2. Click Upload in the Arduino IDE.
3. Once the upload is complete, disconnect IO0 from GND and press the Reset button again.

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Step 4: Accessing Object Detection Results

1. Open the Serial Monitor (Tools > Serial Monitor) and set the baud rate to 115200.
2. View detected objects and their confidence scores in the Serial Monitor.
3. You can expand this by displaying results in a web interface using HTML or integrating with IoT platforms like MQTT.

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Step 5: Enhancing Object Detection

• Model Optimization: Use Edge Impulse or TensorFlow Lite to train custom models optimized for the ESP32-CAM.
• Web Interface: Enhance the project by displaying the detected objects directly in a browser interface.
• IoT Integration: Send detection results to cloud services for further processing or alert systems.

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Troubleshooting

• Model too large: Ensure the model size fits within the ESP32-CAM’s memory.
• Camera not detected: Verify wiring and ensure the camera is connected securely.
• Low accuracy: Train or fine-tune the model with more relevant data.

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Applications of Object Detection with ESP32-CAM

1. Security cameras with motion or object detection
2. Smart home automation (e.g., detecting deliveries or pets)
3. Industrial monitoring and counting systems
4. Wildlife monitoring and research

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Conclusion

You’ve successfully set up basic object detection with the ESP32-CAM. This powerful module, combined with machine learning models, opens up endless possibilities for real-world applications. Experiment with different models, improve accuracy, and expand the functionality to suit your project needs!