MilliNet: Applied Deep Learning Technique for Millimeter-Wave Based Object Detection and Classification

ABSTRACT

Navigation and surveillance are crucial aspects of various fields such as the transportation, defence, and aeronautical industries. Imaging the terrain and the surrounding environment is a key part of a good navigation system. Some of the most widely used imaging techniques are visible light and Light Imaging Detection and Ranging (LiDAR) imaging, which covers two- and three-dimensional imaging. These imaging techniques face difficulties in adverse weather and atmospheric conditions, so there is a need for stronger radiation formation. Moreover, the data collected by radars, which emit these radiations cannot be used directly due to their disorganized characteristic. This article introduces the use of millimeter-wave imaging for the purpose of navigation and surveillance. Millimeter-wave images are captured by radar and fed to a novel neural network architecture, known as MilliNet, which is trained using LiDAR data (in point clouds) and can be used to classify and segment millimeter-wave imagery input involving features such as permutation and symmetric invariance. This provides a three-dimensional image in adverse weather conditions and conveniently uses that data for object detection and classification. The results obtained after testing the model on a dataset have been validated for different conditions and objects.

KEYWORDS:

• Conditional Generative Adversarial Networks
• LiDAR
• Millimeter-wave imagery
• MilliNet
• Point clouds

Acknowledgment

Syed Maaiz Syed Shabbeer Basha and Srivatsan Sridhar have contributed equally.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Syed Maaiz Syed Shabbeer Basha

Syed Maaiz Syed Shabbeer Basha is currently pursuing his BTech in electronics and communication engineering from the National Institute of Technology, Tiruchirappalli (NITT). His research interests include internet of things (IOT), machine learning, pattern recognition, computer vision, and image processing. Email: [email protected]

Srivatsan Sridhar

Srivatsan Sridhar is currently pursuing his BTech in electronics and communication engineering from the National Institute of Technology, Tiruchirappalli (NITT). His research interests include deep learning, natural language processing, internet of things (IOT), and computer vision. Email: [email protected]

Sandeep Kaushik

Sandeep Kaushik is currently pursuing his MTech in electronics and communication engineering from the National Institute of Technology, Tiruchirappalli (NITT). His research interests include deep learning, signal processing, image processing, and communication. Email: [email protected]

Hemant Kumar

Hemant Kumar received his BTech. (with honors) degree in electronics and communication engineering from Kurukshetra University and PhD in electrical engineering from Indian Institute of Technology Bombay. Currently, he is working as an assistant professor at NIT Tiruchirappalli. His research interests include broadband antennas, microstrip antennas and arrays, passive microwave circuits, monopulse tracking, microwave imaging, and machine learning in antennas and microwave. He is also serving as a reviewer in a number of national/international journals including IEEE Access, IET Microwave Antennas & Propagation, IETE, etc. He has published many research articles in refereed journals and refereed conference proceedings, and also filed one patent.