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

Maolin Wang, Ho-Cheung Ng, Bob MF Chung, B Sharat Chandra Varma, Manish Kumar Jaiswal, Kevin K Tsia, Ho Cheung Shum, Hayden Kwok-Hay So

Research output: Contribution to conferencePaper

2 Citations (Scopus)


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.
Original languageEnglish
Number of pages4
Publication statusPublished - 18 May 2017



  • Field programmable gate arrays
  • Image segmentation
  • Imaging
  • Real-time systems
  • Atom optics
  • Detectors
  • Hardward

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