  Object detection in natural scenes can be a challenging task. In many
real-life situations, the visible spectrum is not suitable for traditional
computer vision tasks. Moving outside the visible spectrum range, such as the
thermal spectrum or the near-infrared (NIR) images, is much more beneficial in
low visibility conditions, NIR images are very helpful for understanding the
object's material quality. In this work, we have taken images with both the
Thermal and NIR spectrum for the object detection task. As multi-spectral data
with both Thermal and NIR is not available for the detection task, we needed to
collect data ourselves. Data collection is a time-consuming process, and we
faced many obstacles that we had to overcome. We train the YOLO v3 network from
scratch to detect an object from multi-spectral images. Also, to avoid
overfitting, we have done data augmentation and tune hyperparameters.
