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thermal-imaging

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Qualitative & quantitative performance evaluation of traditional image processing and CNN based automatic methods on Pool Boiling IR images of MIT’s Nuclear Reactor Lab by measuring important boiling heat transfer parameters statistically. Work performed in 2019 as part of BSc. Thesis.

  • Updated Mar 14, 2024
  • Jupyter Notebook

The alignment of feature distributions between visible and thermal domains is crucial for achieving effective object detection. Our proposed adaptive detector, integrated into the faster R-CNN architecture, tackles the domain shift problem and enhances overall performance. Experimental evaluations are demonstrated on the KAIST and FLIR ADAS dataset

  • Updated May 30, 2023
  • Python

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