Multi-nanoparticle recognition and tracking based on SPTGAN-YOLOv3 modeling

Nanoparticle tracking techniques based on point spread function (PSF) engineering can monitor the movement of nanoparticles in 3D space in real-time. However, increasing the axial tracking depth decreases the signal-to-noise ratio (SNR), which makes the reconstruction of nanoparticle trajectories more difficult. This paper proposes a deep learning-based object detection model SPTGAN-YOLOv3 for nanoparticle tracking technique. The SPTGAN-YOLOv3 model utilizes a noisy image with a low SNR as its input. It learns the mapping from noisy to noise-free images, generating a high SNR image. This rendered image is then used as the input for the object detection network, enabling the recognition and tracking of nanoparticles. Comparative analysis with conventional particle tracking methods demonstrates that the SPTGAN-YOLOv3 model significantly improves the precision and efficiency in recognizing and tracking multiple nanoparticles. Moreover, experimental results indicate that this technique notably enhances the SNR of nanoparticle tracking images and enables precise identification of nanoparticles.