Flexible machine/deep learning microservice architecture for industrial vision-based quality control on a low-cost device

This paper aims to delineate a comprehensive method that integrates machine vision and deep learning for quality control within an industrial setting. The proposed innovative approach leverages a microservice architecture that ensures adaptability and flexibility to different scenarios while focusing on the employment of affordable, compact hardware, and it achieves exceptionally high accuracy in performing the quality control task and keeping a minimal computation time. Consequently, the developed system operates entirely on a portable smart camera, eliminating the need for additional sensors such as photocells and external computation, which simplifies the setup and commissioning phases and reduces the overall impact on the production line. By leveraging the integration of the embedded system with the machinery, this approach offers real-time monitoring and analysis capabilities, facilitating the swift detection of defects and deviations from desired standards. Moreover, the low-cost nature of the solution makes it accessible to a wider range of manufacturing enterprises, democratizing quality processes in Industry 5.0. The system was successfully implemented and is fully operational in a real industrial environment, and the experimental results obtained from this implementation are presented in this work.