Small Defect Detection with Computer Vision

Detecting small defects in home inspections can be challenging due to limited visual information and annotation difficulties, but effective strategies exist to enhance detection. These strategies include using high-resolution images, careful annotation with tools like SAM3, and employing data augmentation techniques such as tiling and AI-generated synthetic images to expand the diversity of training data. SAHI (Slicing Adaptive Inference) further improves the detection of subtle defects by slicing images into smaller tiles during inference, thus enhancing the model's ability to spot minor damage. These techniques, which prioritize maximizing visual information during both training and inference, are applicable beyond home inspections to fields like manufacturing and agriculture, where identifying small defects is crucial.