Real-Time Small Object Detection with YOLOv8 + SAHI on Video
🚀 Just Completed: Real-Time Small Object Detection with YOLOv8 + SAHI on Video! 🎯
I'm excited to share my latest project that combines the power of Ultralytics YOLOv8 and SAHI (Slicing Aided Hyper Inference) for enhanced object detection in high-resolution or densely populated videos.
🔍 Problem (Situation):
Detecting small or overlapping objects in high-resolution video is a common challenge in computer vision. Models like YOLOv8 often miss these details in complex scenes such as crowds, drone footage, or agricultural fields.
🎯 Goal (Task):
My objective was to build a real-time object detection system that improves the accuracy of detecting small objects while maintaining performance on large videos.
⚙️ Approach (Action):
To solve this, I integrated YOLOv8 with SAHI (Slicing Aided Hyper Inference) — a method that slices video frames into smaller chunks before detection, improving model focus. I developed a complete Python pipeline using OpenCV and PyTorch that performs detection, overlays bounding boxes and labels, and saves an annotated output video.
✅ Impact (Result):
The final system significantly improved detection accuracy on small and dense objects without compromising speed. It’s now well-suited for use cases like drone surveillance, crowd monitoring, and precision agriculture.
📂 GitHub (Code & README): https://lnkd.in/d5bdMatg