🛡️ Built an AI-powered Logo & Watermark Detection System using YOLOv8 + Streamlit

I’ve been experimenting with computer vision pipelines recently and built a project called VisionGuard AI — a system for detecting logos and watermarks in images, videos, and real-time feeds.

The main goal was to learn and explore:

• YOLOv8 object detection workflows
• Real-time inference optimization
• Synthetic dataset generation
• Streamlit dashboard design
• Video/image processing pipelines
• Model evaluation metrics (mAP, precision, recall)

Features

• Real-time logo/watermark detection
• Video & image processing
• Streamlit-based dashboard
• Synthetic dataset generator
• Training + evaluation scripts
• Modular project structure for experimentation

Tech Stack

• Python
• YOLOv8
• OpenCV
• PyTorch
• Streamlit

One interesting challenge was improving detection consistency on semi-transparent watermarks and low-opacity overlays.

I’d really appreciate feedback from the community on:

• Better approaches for watermark segmentation/removal
• Improving small-object detection accuracy
• Dataset augmentation ideas
• Real-time optimization techniques

Would also love suggestions for future improvements or production deployment ideas.

GitHub repo: https://github.com/Amit123103/Logo_watermark_detection