How to Use SAHI to Detect Small Objects

Detecting small objects in computer vision is challenging yet crucial for various applications, and Slicing Aided Hyper Inference (SAHI) offers a method to enhance detection accuracy by performing inference on image segments and combining their results. The guide outlines how SAHI, in conjunction with the Python package supervision, can be applied across different models to improve object detection, particularly for smaller items in images such as those in aerial surveys or crowded beaches. The process is model-agnostic, supporting various frameworks like Roboflow Inference, Ultralytics YOLOv8, and Transformers, and involves setting up a model, running inference with a slicer, and visualizing results to demonstrate the enhanced detection capability. By leveraging SAHI, users can significantly increase the detection rate of small objects in images, as illustrated through examples comparing results with and without this approach.