Redson Dev · Idea
Smart City Sign Monitoring with Computer Vision
Published July 19, 2026
This project offers a practical way for local businesses and municipalities to monitor the condition and visibility of essential signage. Using computer vision on a low-cost device, it can detect issues like graffiti, damage, or obstructions (e.g., overgrown vegetation). Think of a small historical society in Boston using this to automatically alert staff when their antique district signs need cleaning or repair.
What you'll need
- 1x Raspberry Pi 4 Model B (2GB RAM minimum)
- 1x Raspberry Pi Camera Module 3
- 1x 32GB microSD card
- 1x 5V 3A USB-C power supply
- 1x Weatherproof enclosure for Raspberry Pi and camera
- 1x Mounting bracket for pole or wall
- Jumper wires (for optional external sensor)
Step-by-step
- 01
Set Up Raspberry Pi OS and Camera
Flash Raspberry Pi OS Lite (64-bit) to your microSD card using Raspberry Pi Imager. Insert the card into the Pi, connect the camera module to the CSI port, and power on. Access the Pi via SSH. Run 'sudo raspi-config', navigate to 'Interface Options', and enable 'Camera'.
- 02
Install Computer Vision Libraries
Update your package list with 'sudo apt update && sudo apt upgrade -y'. Install Python3 and pip if not already present. Then, install OpenCV for Python: 'pip3 install opencv-python'. We'll also need 'numpy' for image processing: 'pip3 install numpy'.
- 03
Develop Image Capture and Processing Script
Write a Python script that captures an image from the camera, applies a simple edge detection filter (e.g., Canny), and analyzes object contours to identify changes or anomalies. Use 'picamera2' library for camera interfacing. For instance, capture an image: 'camera.capture_file("sign_image.jpg")'. Then, use OpenCV to load: 'cv2.imread("sign_image.jpg")' and process.
- 04
Implement Anomaly Detection Logic
Define thresholds for what constitutes an 'anomaly'. For graffiti, this might be a high density of unexpected contours in a specific region of interest. For damage, it could be a significant change in the overall shape or texture. Store baseline images and compare new captures against them to detect deviations. If an anomaly is detected, save the image with a timestamp.
- 05
Set Up Notification System
Integrate a simple notification system. For a basic setup, this could be sending an email using Python's 'smtplib' library to a predefined address. Alternatively, save the anomaly images to a shared cloud drive (e.g., using 'rclone') for remote review by the historical society staff. Schedule the script to run periodically using cron, e.g., daily at dawn and dusk.
Tips
- Mount the camera securely and ensure it has a clear, unobstructed view of the sign.
- Consider adding a small solar panel and battery pack for remote deployments without easy access to power.
- Experiment with different OpenCV filters (e.g., thresholding, contour finding, template matching) to fine-tune anomaly detection for specific sign types.