How to detect small objects with SAHI and YOLO?
Hey there reader, if you’re into computer vision and machine learning, you’ve probably faced the frustrating challenge of detecting small objects in images or videos. Whether you’re working with pre-trained models or custom ones, small objects often slip through the cracks, especially when they’re far from the camera. But worry not, because today we’re diving into a game-changing solution: the SAHI (Slicing Aided Hyper Inference) framework for Small Object Detection.
SAHI is designed to optimize object detection algorithms for large-scale and high-resolution imageries.
The Problem: Detecting Small Objects
Object detection has come a long way, enabling numerous applications from autonomous driving to security surveillance. However, a persistent problem remains — detecting small objects. Traditional object detection models, like YOLO (You Only Look Once), struggle with this task. When objects are tiny or distant, the models often miss them or detect them with low confidence.
- Why is this the case? It boils down to the resolution and scale…
