Slicing Aided Hyper Inference (SAHI) for Small Object Detection | Explained

Small object detection is a challenging subfield of computer vision, particularly in surveillance applications, as traditional object detectors often struggle with accuracy due to limited receptive fields, spatial resolution, and class imbalance. The open-source framework Slicing Aided Hyper Inference (SAHI) addresses these issues by employing a novel approach that divides images into overlapping patches, thereby enhancing the pixel area and contextual information of small objects during detection. This method includes slicing-aided fine-tuning, which augments datasets by extracting and resizing patches to improve the detection and localization of small objects in high-resolution images. SAHI's integration into object detection pipelines has been shown to significantly improve average precision across various detectors and datasets, making it particularly effective for applications such as surveillance, autonomous driving, robotics, medical imaging, and wildlife conservation.

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