Deep Learning-Based Image Recognition for Electronic Components Identification

The use of deep learning techniques in the field of picture recognition for the purpose of identifying electronic components. Because of the growing complexity and variety of electronic devices, it is essential for manufacturing, maintenance, and quality control to be able to identify components in a way that is both efficient and accurate. This is accomplished through the utilization of deep learning algorithms, the utilization of convolutional neural networks (CNNs), and advanced image processing techniques in order to improve recognition skills. The suggested model displays higher accuracy in differentiating diverse electronic components, overcoming problems such as differences in size, orientation, and lighting conditions. Moreover, the model succeeds in overcoming these challenges effectively. Based on the findings, it appears that deep learning-based image recognition provides a strong solution for automating the identification process in electronic component analysis. This, in turn, contributes to better efficiency and reliability in the electronics industry. This research makes a contribution to the expanding field of computer vision and highlights the potential of deep learning in the advancement of electrical component identification systems.