300 GHz radar object recognition based on deep neural networks and transfer learning

Marcel Sheeny, Andrew Michael Wallace, Sen Wang

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)
120 Downloads (Pure)


For high-resolution scene mapping and object recognition, optical technologies such as cameras and LiDAR are the sensors of choice. However, for future vehicle autonomy and driver assistance in adverse weather conditions, improvements in automotive radar technology and the development of algorithms and machine learning for robust mapping and recognition are essential. In this study, the authors describe a methodology based on deep neural networks to recognise objects in 300 GHz radar images using the returned power data only, investigating robustness to changes in range, orientation and different receivers in a laboratory environment. As the training data is limited, they have also investigated the effects of transfer learning. As a necessary first step before road trials, they have also considered detection and classification in multiple object scenes.
Original languageEnglish
Pages (from-to)1483-1493
Number of pages11
JournalIET Radar, Sonar and Navigation
Issue number10
Early online date17 Sept 2020
Publication statusPublished - 1 Oct 2020

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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