Improvised Aerial Object Detection approach for YOLOv3 Using Weighted Luminance

Remote sensing is the image acquisition of a target without having physical contact with it. Nowadays remote sensing data is widely preferred due to its reduced image acquisition period. The remote sensing of ground targets is more challenging because of the various factors that affect the propagation of light through different mediums from a satellite acquisition. Several Convolutional Neural Network-based algorithms are being implemented in the field of remote sensing. Supervised learning is a machine learning technique where the data is labelled according to their classes prior to the training. In order to detect and classify the targets more accurately, YOLOv3, an algorithm based on bounding and anchor boxes is adopted. In order to handle the various effects of light travelling through the atmosphere, Grayscale based YOLOv3 configuration is introduced. For better prediction and for solving the Rayleigh scattering effect, RGB based grayscale algorithms are proposed. The acquired images are analysed and trained with the grayscale based YOLO3 algorithm for target detection. The results show that the grayscale-based method can sense the target more accurately and effectively than the traditional YOLOv3 approach.