Improve Small Object Detection Accuracy with Sliced Inference
📝 YouTube Video Description:
🚀 Real-Time Object Detection on Video using YOLOv8 + SAHI (Sliced Inference)
In this video, we demonstrate how to combine the power of Ultralytics YOLOv8 with SAHI (Slicing Aided Hyper Inference) to detect small and densely packed objects in high-resolution videos using Python and OpenCV.
🔍 This technique enhances detection performance by slicing frames into smaller sections, allowing the model to focus better on each region before stitching the results back together.
🔧 Technologies Used:
🧠 YOLOv8 (Ultralytics)
🧩 SAHI (Sliced Inference)
🐍 Python
📹 OpenCV
🧠 PyTorch
💻 What You’ll See in This Video:
Load a YOLOv8 model using SAHI’s AutoDetectionModel
Process video files frame-by-frame
Run real-time sliced object detection
Save results with bounding boxes & labels into a new video file
Optional real-time display during processing
📁 Project Repository:
🔗 GitHub:https://github.com/TLILIFIRAS/Improve-Smal...
🧪 Use Case Examples:
✅ Crowd counting
✅ Precision agriculture
✅ Drone surveillance
✅ Traffic monitoring
✅ Wildlife detection