Authors | G. Kumar1, S. Kumar2 , A. Sharma3, A. Raturi4 , A. Shrivastava5, A.L.N. Rao6, A.K. Khan7 |
Affiliations |
1Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh,174103, India 2Centre of Research Impact and Outcome, Chitkara University, Rajpura-140401, Punjab, India 3Department of Mechanical Engineering, GLA University, Mathura- 281406, Uttar Pradesh, India 4Department of Mechanical Engineering, Graphic Era Deemed to be University, Dehradun, India 5Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, TNIndia 6Lloyd Institute of Engineering & Technology, Greater Noida, India 7Lloyd Law College, Greater Noida, India |
Е-mail | gauravcivilengg714@gmail.com |
Issue | Volume 16, Year 2024, Number 6 |
Dates | Received 27 August 2024; revised manuscript received 20 December 2024; published online 23 December 2024 |
Citation | G. Kumar, S. Kumar, A. Sharma, et al., J. Nano- Electron. Phys. 16 No 6, 06008 (2024) |
DOI | https://doi.org/10.21272/jnep.16(6).06008 |
PACS Number(s) | 61.46.Df, 68.37.Hk |
Keywords | Nanoparticle (77) , Scanning Electron Microscopy (SEM), Synthetic Data, Multi fused Spectral Deep Convolute Neural Net (MS-DCNN). |
Annotation |
Nanoparticle detection in scanning electron microscopy (SEM) is crucial for various applications. Existing techniques for detecting nanoparticles in SEM images need help to handle dispersed particles and need more accuracy. This research uses a deep learning strategy to enhance recognition efficiency and precision. To overcome the challenges, we develop a robust Multi fused Spectral Deep Convolute Neural Net (MS-DCNN) based model for nanoparticle detection, utilizing synthetic data generation to facilitate practical neural network training and collecting the SEM image dataset for detecting the nanoparticle. Created an algorithm to generate synthetic data, combining random particle distributions to simulate SEM micrographs and allows the development of annotated datasets that are essential for neural network training. Compared to existing approaches; the results are reduced pixel (0.62), warp errors (0.0008), decreased computing time (398s) and greater accuracy (92.5%). The suggested MS-DCNN framework is practical and better than conventional techniques, exhibiting improved precision in the identification of dispersed nanoparticles. The generation of synthetic data helps in the development of a trained model that will deal with a variety of particle distributions. The model is trained using synthetic data, demonstrating the technique's potential to improve nanoparticle analysis in SEM imaging which got proven result over the existing method. |
List of References |