Real-time object detection and classification for high-speed asymmetric-detection time-stretch optical microscopy on FPGA

Abstract

A real-time object detection and classification system using FPGA developed for high-speed asymmetric time-stretched optical microscopy (ATOM) framework is presented. Due to the massive amount of data generated by optical frontend, storing the raw data for offline post-processing is slow and impractical for the targeted single cell analysis applications. The proposed FPGA solution eliminates the need to transfer and persist the entire raw data by processing low-level signals and forming high-level images in real-time. Objects of interest are detected and segmented from the image stream and a classifier subsequently performs high-level analysis on the segmented images. When compared with existing software-based post-processing workflow, this FPGA-based approach will improve both the number of objects captured per experiment and the overall end-to-end object classification performance. The system also allows co-optimization between optical system, low-level signal processing and image analytic in a unified environment that enables new scientific discoveries previously unachievable.

Publication
2016 International Conference on Field Programmable Technology (FPT)
Maolin Wang
Maolin Wang
PhD Candidate
Ho-Cheung Ng
Ho-Cheung Ng
M.Phil, PhD Candidate (ICL)
B. Sharat Chandra Varma
B. Sharat Chandra Varma
PhD, PostDoc Research Fellow
Manish Kumar Jaiswal
Manish Kumar Jaiswal
PhD, Research Scientist
Hayden Kwok-Hay So
Hayden Kwok-Hay So
Associate Professor