Improvised Aerial Object Detection approach for YOLOv3 Using Weighted Luminance
This work addresses robustness in aerial object detection for applications such as drone surveillance and remote sensing, though it appears incremental as it modifies an existing method.
The paper tackled the challenge of object detection in aerial imaging by addressing light propagation issues like Rayleigh scattering, introducing adaptive RGB filters based on weighted luminance to improve YOLOv3, resulting in more accurate and efficient detection compared to the traditional approach.
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.