Car Detection and Color Identification Using YOLOv8 and OpenCV

The rapid advancement of computer vision and deep learning has paved the way for innovative applications in various domains. One such application is car detection and color identification, which has broad use cases in traffic analysis, parking management, and automotive inventory systems.

This project demonstrates the implementation of a system that detects cars in an image and identifies their dominant colors. Leveraging YOLOv8 for object detection and OpenCV for image processing, the project is deployed as a user-friendly web app using Streamlit.

Technical Implementation

1. Car Detection At the core of the project is the YOLOv8 model, a state-of-the-art object detection framework. Using pre-trained weights (yolov8n.pt), the model is capable of accurately detecting cars in uploaded images.

• The detection pipeline extracts bounding boxes for each detected car and crops the corresponding image regions.

2. Color Identification To determine the dominant color of a detected car, the project employs OpenCV and the HSV color space.

• Color ranges are defined for Red, Green, and Blue hues.
• A mask is created for each color range, and the system detects the dominant color based on pixel intensity thresholds.

3. Deployment with Streamlit To ensure accessibility, the project integrates Streamlit for deployment. Users can upload an image and instantly view the detected cars along with their identified colors.

Results The system successfully detects cars in uploaded images and identifies their dominant colors. It displays cropped car images and outputs color labels, providing a seamless experience.

Applications

• Traffic monitoring: Analyze vehicle flow and color distribution.
• Parking lot management: Track vehicles efficiently.
• Automotive inventory systems: Organize car data based on appearance attributes.

Challenges and Learnings

• Fine-tuning the YOLO model for optimal car detection.
• Handling color detection in varying lighting conditions.
• Integrating machine learning workflows into a web application.

These challenges provided an excellent opportunity to strengthen skills in computer vision, model deployment, and real-world problem-solving.

Conclusion This project is a testament to the power of deep learning and its potential for practical applications. By combining YOLOv8, OpenCV, and Streamlit, we developed a complete end-to-end system that is scalable and impactful.

If you’re interested in building similar systems or discussing innovative AI solutions, feel free to connect!

Code and Resources

The full code for this project is available here.

#ComputerVision #DeepLearning #YOLOv8 #OpenCV #Streamlit #MachineLearning #AI

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