## Overview
This project aims to detect license plates in images using the YOLOv8 model and extract text from the detected license plates. It includes the complete workflow from data preparation and model training to model deployment using OpenVINO. The main components of this project include:
1. **Data Preparation:**
- Collect and preprocess a dataset containing images with license plates and labels for car/non-car objects.
2. **Model Training:**
- Train the YOLOv8 model on the prepared dataset for license plate and car detection.
3. **License Plate Text Extraction:**
- Implement Optical Character Recognition (OCR) to extract text from detected license plates.
4. **Model Selection:**
- Evaluate multiple trained models and select the best-performing one based on detection accuracy and OCR performance.
5. **Model Format Conversion:**
- Convert the selected model to various formats, including ONNX, and quantize it for optimized inference.
6. **OpenVINO Integration:**
- Use the OpenVINO toolkit to optimize the model for deployment on Intel hardware.
7. **Deployment:**
- Deploy the OpenVINO-optimized model for real-time license plate detection and text recognition.
## Getting Started
These instructions will help you get a copy of the project up and running on your local machine for development and testing purposes.
### Prerequisites
Before you begin, make sure you have the following prerequisites installed:
- Python 3.x
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车牌识别_基于YOLOv8实现车牌检测+车牌识别算法_附项目源码+详细流程教程_优质项目实战.zip (55个子文件)
车牌识别_基于YOLOv8实现车牌检测+车牌识别算法_附项目源码+详细流程教程_优质项目实战
runs
detect
val
val_batch0_pred.jpg 577KB
F1_curve.png 93KB
val_batch2_labels.jpg 621KB
R_curve.png 96KB
P_curve.png 87KB
val_batch0_labels.jpg 574KB
val_batch2_pred.jpg 626KB
PR_curve.png 83KB
confusion_matrix_normalized.png 88KB
val_batch1_pred.jpg 620KB
val_batch1_labels.jpg 610KB
confusion_matrix.png 81KB
train
events.out.tfevents.1696938460.038b34ad9335.1419.0 88B
labels_correlogram.jpg 179KB
args.yaml 1KB
labels.jpg 98KB
train3
val_batch0_pred.jpg 579KB
weights
best.pt 134B
last.pt 134B
F1_curve.png 92KB
results.csv 27KB
R_curve.png 95KB
events.out.tfevents.1696938635.038b34ad9335.1419.1 1000KB
train_batch0.jpg 528KB
P_curve.png 86KB
val_batch0_labels.jpg 576KB
train_batch2662.jpg 464KB
train_batch1.jpg 574KB
PR_curve.png 83KB
results.png 343KB
train_batch2.jpg 528KB
labels_correlogram.jpg 185KB
confusion_matrix_normalized.png 88KB
val_batch1_pred.jpg 627KB
train_batch2661.jpg 458KB
args.yaml 1KB
train_batch2660.jpg 491KB
val_batch1_labels.jpg 622KB
confusion_matrix.png 81KB
labels.jpg 134KB
predict
Cars2_png.rf.7ae57b5bb53835463ae63245cacfa0b7.jpg 94KB
predict2
Cars2_png.rf.7ae57b5bb53835463ae63245cacfa0b7.jpg 94KB
predict3
Cars2_png.rf.7ae57b5bb53835463ae63245cacfa0b7.jpg 94KB
train2
args.yaml 1KB
License and Car Detection.ipynb 4.38MB
dog.jpeg 104KB
demo_target.mp4 3.77MB
model
fingerprint.pb 127B
variables
variables.index 145B
variables.data-00000-of-00001 9KB
requirements.txt 320B
Formats.ipynb 224KB
License Datection.ipynb 9.18MB
README.md 1KB
demo.mp4 9.12MB
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