Embedded implementation of a random feature detecting network for real-time classification of time-of-flight SPAD array recordings

Joyce Mau, Saeed Afshar, Tara Julia Hamilton, André van Schaik, Rudi Lussana, Aaron Panella, Jochen Trumpf, Dennis Delic

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

3 Citations (Scopus)


A real time program is implemented to classify different model airplanes imaged using a 32x32 SPAD array camera in time-of-flight mode. The algorithm uses random feature extractors in series with a linear classifier and is implemented on the NVIDIA Jetson TX2 platform, a power efficient embedded computing device. The algorithm is trained by calculating the classification matrix using a simple pseudoinverse operation on collected image data with known corresponding object labels. The implementation in this work uses a combination of serial and parallel processes and is optimized for classifying airplane models imaged by the SPAD and laser system. The performance of different numbers of convolutional filters is tested in real time. The classification accuracy reaches up to 98.7% and the execution time on the TX2 varies between 34.30 and 73.55 ms depending on the number of convolutional filters used. Furthermore, image acquisition and classification use 5.1 W of power on the TX2 board. Along with its small size and low weight, the TX2 platform can be exploited for high-speed operation in applications that require classification of aerial targets where the SPAD imaging system and embedded device are mounted on a UAS.

Original languageEnglish
Title of host publicationLaser Radar Technology and Applications XXIV
EditorsMonte D. Turner, Gary W. Kamerman
Place of PublicationBellingham, WA
Number of pages13
ISBN (Electronic)9781510626768
ISBN (Print)9781510626751
Publication statusPublished - 2019
EventLaser Radar Technology and Applications XXIV 2019 - Baltimore, United States
Duration: 16 Apr 201917 Apr 2019

Publication series

NameProceedings of SPIE
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


ConferenceLaser Radar Technology and Applications XXIV 2019
CountryUnited States


  • LiDAR
  • convolutional layer
  • embedded computing
  • SPAD
  • Single photon avalanche diode
  • UAS
  • time-of-flight
  • classification

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