Defect Detection Dataset: Porosities in Machined Aluminum Holes

This dataset comprises 302 JPEG images captured with an endoscopic camera, focusing on detecting porosities in the machined holes inner walls of cast aluminum parts. Each image has a resolution of 400x400 pixels in RGB color space, providing detailed views of potential defects. The dataset is intended for developing and evaluating algorithms for automated defect detection in industrial manufacturing, specifically targeting porosity defects in aluminum casting processes. It does not include annotations or labels. Researchers can use these images to: Train and test machine learning models for defect detection. Explore characteristics and distributions of porosity defects in machined holes. Develop algorithms for automated quality control in manufacturing settings. Preprocessing such as normalization and resizing may be necessary before applying the images to machine learning tasks. Data was collected using SF-CQ6USB-D2.0 Endoscopic camera.