Detecting and classifying defects on the surface of water heaters: Development of an automated system

Abstract

Seeking a total automation of the existing industrial processes, manual product quality control systems have been gradually replaced by automated ones, to significantly improve efficiency and speed, and ultimately, increase industrial productivity. 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 – for detection and classification – 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. Overall, this system demonstrated great potential as a suitable alternative to the existing manual inspection of the painted surfaces of Bosch Thermotechnology water heaters.