How bhaskrr/traffic-sign-detection-using-yolov11 works
This repository is a small YOLOv11 traffic-sign detection demo with separate image and video workflows. Users launch either script; each loads the fine-tuned model, runs detection, annotates results, and displays them. The video workflow reads the bundled traffic-sign clip, while image input is configured in the script but its referenced sample image is absent from the tree. Training is documented, but its notebook and dataset are not included, so only the supplied model artifact is shown.
Main entry points: process_video.py Video workflow · process_image.py Image workflow · traffic_sign_detector.pt Fine-tuned weights · traffic_signs.mp4 Traffic-sign clip
Components 6
Image detection
- Image workflow
process_image.py
Video detection
- Video workflow
process_video.py - Traffic-sign clip
data/input/traffic_signs.mp4
Detection model
- Fine-tuned weights
model/traffic_sign_detector.pt
Other components
- User
- OpenCV display
Connections 7
- User → Image workflow: runs
README.md - User → Video workflow: runs
README.md - Image workflow → Fine-tuned weights: loads
process_image.py - Video workflow → Fine-tuned weights: loads
process_video.py - Video workflow → Traffic-sign clip: reads
process_video.py - Image workflow → OpenCV display: shows annotations
process_image.py - Video workflow → OpenCV display: shows frames
process_video.py