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Aerial imaging plays a crucial role in navigation and data acquisition forunmanned aerial vehicles and satellite imaging systems. In recent days, theemployment of drones has been escalated in several applications that are notlimited to surveillance, delivery systems, aerial warfare, and agriculturalactivities. Aerial imaging of ground targets is highly challenging because ofvarious factors that affect light propagation through different mediums.Several convolutional neural network-based object detection algorithms that aredeveloped require more robustness when applied in the field of aerial imagingand remote sensing. In order to handle the adverse effects of light propagationwith respect to time and solar radiance, adaptive RGB filters for grayscaleimaging based on weighted luminance are introduced that extensively solve theproblem of rayleigh scattering effect. Images of objects that are easilydiminished by rayleigh scattering are acquired in various timezones. Theacquired images are labelled precisely and subjected to training andvalidation. The results show that the proposed method detects the object moreaccurately and efficiently than the traditional YOLOv3 approach.

Improvised Aerial Object Detection approach for YOLOv3 Using Weighted Luminance