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In this regard, an automated inspection system was developed in this work to detect and classify defects on the painted surfaces of Bosch Thermotechnology water heaters. This system comprised a deflectometry-based image acquisition module, two light deep learning models built and trained from scratch for defect detection and classification in the painted surfaces and a visual interface. The experimental results confirmed that: (1) deflectometry techniques were crucial for an accurate defect detection; (2) the two lightweight models \u2013 for detection and classification \u2013 rapidly achieved high accuracies, even in the testing stage, demonstrating their high performance regardless of their small size; (3) the developed system was able to correctly and quickly predict the status of a painted surface, and then successfully send this status information to a user-friendly visual interface, validating its suitability for an industrial setting. 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