https://github.com/ky28059/stop-sign-detection
Feature-based stop sign detection?
https://github.com/ky28059/stop-sign-detection
Last synced: 3 months ago
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Feature-based stop sign detection?
- Host: GitHub
- URL: https://github.com/ky28059/stop-sign-detection
- Owner: ky28059
- License: mit
- Created: 2024-07-31T01:48:44.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-07-31T01:52:41.000Z (11 months ago)
- Last Synced: 2024-07-31T04:31:58.386Z (11 months ago)
- Language: Python
- Size: 16.4 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# stop-sign-detection
To run:
- Place images containing stop signs in `./samples/positive` (some have already been provided).
- Place images *not* containing stop signs in `./samples/negative` (some have already been provided).
- Run `test.py` to iterate through each image and see what features the algorithm detects!### About
The idea behind this project is loosely based around [this OpenCV guide about FLANN-based feature matching](https://docs.opencv.org/4.x/d5/d6f/tutorial_feature_flann_matcher.html),
using OpenCV's [ORB](https://docs.opencv.org/4.x/d1/d89/tutorial_py_orb.html) keypoint detector instead of the SURF
detection used in the guide for licensing reasons.Do stop signs naturally lend themselves to feature tracking? Not *really*. But there are seemingly enough features in
the stop sign text to differentiate stop-sign photos from non stop-sign photos with decent accuracy.
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### Alternatives to consider
- Using an OCR algorithm like [tesseract](https://pypi.org/project/pytesseract/) to detect the word "stop" in each image,
though it will take a long time to run (probably insufficient for RACECAR purposes).
- Training a CNN on stop sign images; this will likely be faster due to the TPU on the racecars.