Printed Circuit Boards (PCBs) are crucial functional components in electrical devices that require inspection during production to prevent faulty products. However, Automated Optical Inspection (AOI) machines for PCB inspection are costly, especially for small production lines. Previous studies showed that image processing and computer vision techniques could provide cheaper alternatives for PCB optical inspection, albeit with the respective limitations in the research field. The proposed project combines the development of a colour-assisted image subtraction algorithm with the use of hardware support to produce a complete system with PCB counting and major missing component detection functions for the real-time environment. The hardware aspect included the use of the Raspberry Pi, a USB camera, a sensor-equipped conveyor and a custom camera stand to address several limitations in the research field. Testing and validation procedures were performed on the complete system such as function validation and comparative testing. The results of the experimental test procedures showed promising results with the average accuracy exceeding 90% for both the PCB counting and the major missing component detection functions. Overall, the project explored the use of colour in computer vision for PCB inspection, with hardware support for the real-time environment that could be expanded in future work.