PT Unknown
AU Rafael E. Rivadeneira
   Henry Velesaca
   Angel Sappa
TI Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach
BT 17th International Conference on Signal-Image Technology & Internet-Based Systems
PY 2023
DI 10.1109/SITIS61268.2023.00056
AB This work proposes a novel approach that integrates super-resolution techniques with off-the-shelf object detection methods to tackle the problem of handling very low-resolution thermal images. The suggested approach begins by enhancing the low-resolution (LR) thermal images through a guided super-resolution strategy, leveraging a high-resolution (HR) visible spectrum image. Subsequently, object detection is performed on the high-resolution thermal image. The experimental results demonstrate tremendous improvements in comparison with both scenarios: when object detection is performed on the LR thermal image alone, as well as when object detection is conducted on the up-sampled LR thermal image. Moreover, the proposed approach proves highly valuable in camouflaged scenarios where objects might remain undetected in visible spectrum images.
ER