<![CDATA[Early detection of defects through preventive maintenance is important in industry to avoid economic losses, as in the case of ceramic tile manufacturing, where manual inspection allows defective parts to advance in production, causing delays. The research review shows that computer vision enables the automation of object detection, classification, and elimination tasks in industrial processes, using solutions based on Python, OpenCV, and MATLAB. For this reason, the design of a computer vision recognition system with OpenCV is proposed, which allows automatic discarding of ceramics with defects using an algorithm for detecting ceramics with a camera and Arduino-based hardware, comparing the captured images with a standard image on a conveyor belt. The machine vision system was integrated with a camera connected to a computer running OpenCV, achieving effective automatic detection with a threshold of 25% difference from the standard part. This percentage was calculated by comparing the grayscale pixel values with a reference image. The system calculates the proportion of pixels that exceed the similarity threshold. The conclusion is that the developed system contributes to production, highlighting the possibility of future industrial integration.]]>
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