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Named after muhammad al-khorezmi tashkent university of information technologies fergana branchBog'liq m12Necessary hardware:
Camera — Raspberry Pi Camera module v2 (Sony IMX219 8MPx, 1080p30, 720p60)
Edge device — Raspberry Pi 4 Model B 4GB (CPU: Broadcom BCM2711, Quad core Cortex-A72 (ARM v8) 64-bit SoC @ 1.5GHz; RAM: 4GB LPDDR4–3200 SDRAM; 40 pin GPIO header; 2.4 GHz/5.0 GHz 802.11ac Wi-Fi, Bluetooth 5.0)
SD Card (>8GB)
Power Supply — 5V 3A USB-C
How it started: Raspberry Pi with a camera module
Additional:
Heat sinks, cooling fan
UPS
Display (*Waveshare 2.7inch e-Paper HAT)
Relay / Raspberry HAT to control external device (barrier)
Camera mount ("unique metal wire mount for the camera"
Better to use a TFT or OLED type of screen with a decent refresh time, but at that time I only had this one.
Pic.2.2.14. Rpi in a heat-dissipation case + camera module V2 + UPS + e-ink display.
Conclusion on Chapter 2
The process described outlines a comprehensive approach for localizing and extracting license plate information from images using the Viola-Jones method and subsequent normalization steps. Here's a summary of the key steps involved:
1. Contour Analysis and Viola-Jones Method:
- Determine contours in the image and identify areas enclosed by these contours.
- Utilize the Viola-Jones method based on Haar features for object detection in real-time, including license plate areas in complex conditions.
- The method involves moving a window across the image, calculating Haar features in each area, and using a cascade classifier to detect objects based on trained thresholds.
2. Advantages of Viola-Jones Method:
- Effective in localizing number plates under various conditions.
- Analyzes specific characteristics of the number plate area for efficient detection.
3. Disadvantages of Contour Analysis:
- High computational complexity.
- Challenges with accurately defining boundaries, especially in real-world scenarios with unclear outlines.
4. Normalization Process:
- After localizing the license plate area, generate an image containing only the license plate.
- Normalize the image by rotating the rectangular area to align with the image coordinate system.
- Trim the license plate horizontally and vertically, apply filters for noise reduction or contrast enhancement.
5. Angle Determination and Frame Boundaries:
- Use the Hough transform for lines to determine the angle of rotation for the license plate frame.
- Calculate the angle between the resulting straight line and the horizon line.
- Construct intensity histograms horizontally and vertically to find the boundaries of the license plate frame.
By following these steps, you can effectively localize, extract, and normalize license plate information from images, enabling further processing such as optical character recognition (OCR) for reading the license plate numbers accurately. Each step contributes to enhancing the overall efficiency and accuracy of license plate recognition systems in diverse environmental conditions.
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