  Aerial imaging plays a crucial role in navigation and data acquisition for
unmanned aerial vehicles and satellite imaging systems. In recent days, the
employment of drones has been escalated in several applications that are not
limited to surveillance, delivery systems, aerial warfare, and agricultural
activities. Aerial imaging of ground targets is highly challenging because of
various factors that affect light propagation through different mediums.
Several convolutional neural network-based object detection algorithms that are
developed require more robustness when applied in the field of aerial imaging
and remote sensing. In order to handle the adverse effects of light propagation
with respect to time and solar radiance, adaptive RGB filters for grayscale
imaging based on weighted luminance are introduced that extensively solve the
problem of rayleigh scattering effect. Images of objects that are easily
diminished by rayleigh scattering are acquired in various timezones. The
acquired images are labelled precisely and subjected to training and
validation. The results show that the proposed method detects the object more
accurately and efficiently than the traditional YOLOv3 approach.
