Visual Detection System for Industrial Defects

An effective industrial inspection system enhances production efficiency, ensures product quality, reduces costs and boosts the factory’s core competitiveness throughout the entire production process. Ceramic tiles, widely utilized in building decoration, hold a significant market share. Product quality is essential not just for aesthetic appeal but also for building safety. Detecting surface defects in ceramic tiles poses formidable challenges, including complex background textures, low defect saliency and significant variations in defect scales. This presents important research opportunities in the field of computer vision. This article takes this type of difficult product as an example and starts with the ceramic tile dataset to design a template based industrial defect detection algorithm, including an image acquisition module, a template matching module and a defect detection module. A LoFTR-based template matching algorithm is proposed in this paper for the template matching module to accurately collect and match template images. Use the template images and perspective transformations to eliminate interference from background patterns after acquiring image pairs. At the same time, the system’s hardware selection scheme is established, and the YOLOv5 detection network is selected as the baseline model for the performance optimization study.