Low-Cost Object Detection System Using ESP32-CAM – Approach, Challenges

[Danieldsouza]

TL;DR

DIY ESP32-CAM object detection system using motion detection and lightweight processing. Covers setup, challenges like memory and false triggers, and practical optimizations, with a discussion on improving accuracy on constrained hardware.

Hello everyone,

I wanted to share a compact DIY project I’ve been working on recently—an object detection system using the ESP32-CAM module. The goal was to build something low-cost that can perform basic visual detection tasks without relying heavily on cloud processing.

Project Overview

https://www.handsontec.com/dataspecs/module/ESP32-CAM.pdf

The idea is to use the ESP32-CAM for:

• Capturing images/video
• Running lightweight object detection
• Triggering an action (like LED, buzzer, or notification)

This is useful for:

• Basic surveillance setups
• Smart door monitoring
• Entry/exit detection systems

Hardware Used

• FTDI programmer (for uploading code)
• ESP32-CAM module (OV2640 camera)
• Optional: Buzzer / LED for alerts
• Stable 5V power supply (important for reliability)

Working Principle
Since ESP32-CAM has limited processing power, full-scale ML models aren’t practical. Instead, I explored:

1. Basic Motion Detection

• Frame differencing between consecutive images
• Thresholding to detect changes

2. Lightweight Object Detection

• Using pre-trained tiny models (where feasible)
• Or offloading processing to a local server (optional hybrid approach)

3. Trigger Mechanism

• If motion/object is detected → GPIO triggers output

Challenges Faced
Some practical issues I ran into:

Memory Constraints:
Running ML models directly is very limited
Power Stability:
ESP32-CAM is sensitive to voltage drops (caused random resets)
False Positives:
Lighting changes often triggered motion detection
Wi-Fi Latency:
Streaming or sending images can introduce delay

What Helped Improve Results

A few optimizations that made a noticeable difference:

• Adding basic filtering for lighting variation
• Using region-of-interest (ROI) instead of full-frame detection
• External 5V regulated supply instead of FTDI power
• Reducing frame resolution for faster processing

Possible Improvements

I’m currently exploring:

• Integrating with a Raspberry Pi for edge processing
• Using TensorFlow Lite Micro (very limited use cases)
• Event-based image capture instead of continuous streaming
• Better algorithms for distinguishing motion vs actual objects

Looking for Suggestions
I’d love to hear from the community:

1. Any efficient object detection approaches for constrained devices?
2. Better ways to reduce false positives in motion detection?
3. Has anyone successfully deployed TinyML on ESP32-CAM in real scenarios?

This project is still evolving, but it’s been a great exercise in balancing hardware limitations vs functionality. Hopefully this helps someone working on similar low-cost vision systems.

Looking forward to your thoughts and suggestions!