Convolutional neural networks for electronic component classification
DOI:
https://doi.org/10.5377/ri.v1i11.18460Keywords:
Electronic components, Convolutional neural networks, Artificial neural networks, Computer vision, MobileNetV2Abstract
This article presents an approach based on convolutional neural networks (CNN) for classifying electronic components. The objective is to develop an intelligent system capable of identifying electronic components in diverse environments. This task can benefit process efficiency in education and inventory control applications in the electronics industry. The dataset was created using both internet and proper images to improve the training and evaluation of the proposed model. The results were then compared to those of the pre-trained network MobileNetV2. The findings indicate that the artificial neural networks achieved high accuracy in detecting electronic components by using the transfer learning technique.
97
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- NonCommercial — You may not use the material for commercial purposes .
- ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
