How to Detect Small Objects

Detecting small objects in computer vision is challenging due to their diminutive size, limited information, and the inadequacy of standard datasets and models that focus on larger objects. Slicing Aided Hyper Inference (SAHI) is proposed as a solution, which involves dividing images into overlapping slices, applying object detection models to each slice, and merging the results for comprehensive detection across the entire image. This method, which is model-agnostic, leverages existing state-of-the-art models and enhances small object detection without requiring training on larger images. The technique increases the number of forward passes, leading to more predictions and improved recall and F1-score, especially for small objects, as evidenced by its application on the VisDrone dataset using YOLOv8. Despite the trade-off of increased computation, SAHI's ability to improve detection accuracy makes it a valuable tool in scenarios with many small objects, allowing for potential correction of missing ground truth labels through human-in-the-loop workflows.