Surface Defect Detection

For a long time, product quality control relies on manual examination in the manufacturing field. In the past several decades, automated surface inspection (ASI) have become more widespread as hardware and software technologies have developed rapidly. To lower labor costs and enhance examination efficiency, more factories start to employ embedded machines for product inspection. ASI platforms use special light sources and industrial cameras to capture the images of the product surface, and machine/computer vision technologies to filter out defective products, which can reduce labor greatly. High-performance cross-product ASI algorithms are urgently needed in manufacturing.
In this project, we at saiwa© team investigated several types of Surface Defects using 15 different datasets. These datasets cover defect on surfaces like: Metal, Steel, Polymer and Texture. Multiple classification and segmentation deep networks are employed for each case and dataset. Upon customer agreement (i.e. AI-innovate© ), we also provided a simple UI so that users may try the algorithms freely on their own images and in case of interest, may leave us a customization request to retrain the networks on their specific dataset or other type of surfaces and defects.

Project link: [click here]
Project customer: AI-innovate Company
Project status: Finished (September 2021 to January 2022)