PCB Component Inspection System Using Raspberry Pi

High-performance vision inspection systems that can analyze enormous volumes of unstructured data are in high demand in the field of industrial automation. This project outlines a novel strategy to meet this requirement by emphasizing the application side of image collecting and processing rather than the underlying technology.It is built to function flawlessly in industrial environments while collecting photos from various sources. These photos go through a number of sophisticated image processing techniques because they are frequently filled with complex and unstructured data. The system’s strengths cover a wide range of industrial fields, such as dimensions analysis, defect identification, and object recognition. It offers quick data processing and high levels of accuracy through careful algorithm design and optimization, making it a vital asset in improving industrial operations.For industrial applications, this system handles unorganised data exceptionally well. Industries are able to improve product quality, lower faults, and streamline operations because to its powerful picture analysis capabilities. We highlight the system’s potential to lead to substantial developments and enhancements across many industrial sectors by concentrating on its actual applications. The proposed system uses Raspberry Pi 2 board as the processor along with the image processing algorithms and R-CNN algorithm using OpenCV library in Python for comparison of the images. Thus, the model is trained effectively to meet the requirements and inspect PCB components successfully.