Object Recognition for Multiband Thermal Infrared Sensing
- Resource Type
- Conference
- Authors
- Tulyakov, Sergey; Mitin, Vladimir; Biswal, Gyana; Yakimov, Michael; Tokranov, Vadim; Sablon, Kimberly
- Source
- 2023 IEEE Long Island Systems, Applications and Technology Conference (LISAT) Systems, Applications and Technology Conference (LISAT), 2023 IEEE Long Island. :1-6 May, 2023
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Geoscience
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Image recognition
Image color analysis
Adaptive arrays
Photothermal effects
Cameras
Photodetectors
Sensors
thermal infrared
long wave infrared
quantum well infrared photodetectors
multispectral
fusion
- Language
The object recognition in thermal infrared spectrum can possibly be enhanced by capturing radiation signals in narrower subbands of this spectrum and performing recognition in color or multiple channel thermal infrared images. In this work, we investigate possible benefits of 2-channel thermal infrared images captured by commercial cameras. We performed experiments on our collected images containing persons and cars. Fusion of object recognition results obtained in different channels separately, gives some improvement over the use of a recognizer with single channel full spectrum images. We also present a proofof- concept design of adaptable thermal imager based on asymmetrically-doped double quantum well arrays, which can efficiently capture multiband images in the future.