During the manufacture of composite products, air voids are formed which affect certain mechanical properties of the composite. This paper presents the development of an imaging system for viewing composite products, which includes structured light illumination, image acquisition of the illuminated surface, and scanning of the composite product. This is followed by a theoretical and experimental demonstration of air voids detection using convolutional neural networks. Deep learning was performed using the YOLO system, which is designed to recognize objects on images. All programs for image preprocessing and database generation were developed in the Python software environment. The final weights of the Deep Learning and the functioning of the air voids detection were then successfully tested on test images after the obtained results were evaluated.
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