Electronic component recognition based on microcontroller and machine vision in proteus simulation and analysis

With the development of intelligence and automation technology, automatic identification of electronic components has become increasingly important in manufacturing, maintenance, and quality control. Traditional manual recognition methods are inefficient and prone to errors, making it difficult to meet the demands of large-scale production. Therefore, an automated recognition scheme that combines microcontrollers and machine vision is proposed, which achieves real-time electronic component recognition and classification through image acquisition and processing technology. The system adopts edge detection, feature matching, and template recognition technologies to achieve the recognition of common electronic components such as resistors, capacitors, diodes, etc. The simulation results show that the system can efficiently and accurately identify electronic components, and has high practical value. By combining Proteus simulation with hardware, this study provides an effective solution for the automatic identification of electronic components, suitable for fields such as automated production lines, intelligent detection equipment, and electronic product quality control.