Dynamic one-shot target detection and classification using a pseudo-Siamese network and its application to Raman...

Area of Science:

• Spectroscopy
• Chemometrics
• Machine Learning

Background:

• Raman spectroscopy is crucial for military biological and chemical defense.
• Conventional methods require preprocessing and multiple spectral shots for accuracy.
• Deep learning offers high accuracy but needs retraining for new targets.

Purpose of the Study:

• To develop a deep learning algorithm for Raman spectroscopy-based target detection and classification.
• To address the limitations of conventional methods and existing deep learning approaches, specifically the need for retraining.
• To achieve single-shot detection and classification without preprocessing.

Main Methods:

• A novel algorithm based on a variant of the pseudo-Siamese network was developed.
• The algorithm was designed to detect and classify targets using only one spectral shot.
• No spectral preprocessing was required for the algorithm's operation.

Main Results:

• The algorithm successfully detected and classified targets with a single spectral shot.
• The method eliminated the need for spectral preprocessing.
• Retraining was not necessary for identifying untrained target classes.

Conclusions:

• The proposed pseudo-Siamese network variant offers an efficient and adaptable solution for Raman spectroscopy-based target detection.
• This approach significantly reduces processing time and complexity compared to conventional and other deep learning methods.
• The algorithm demonstrates potential for real-time applications in defense and security scenarios.