Object Detection using Ryze Tello Drone with Help of Mask-RCNN

Now a days UNMANNED AERIAL VEHICLES incorporating image / video segmentation and object detection methods in them for various applications such as reconnaissance, surveillance, tracking, search and rescue operations in the recent years convolutional neural networks usage has been increased leading to develop rcnn (regional convolutional neural networks) which have gained interest in the research community due to its reliability and robust class of recognizing image content even after rcnn we come across state of the art object detection methods such as fast rcnn, faster rcnn, masked rcnn which are its successors which are known for their speed and accuracy (in the year masked rcnn neural network was developed which is the successor of faster rcnn and in this paper) by utilizing Mask rcnn neural network algorithm we illustrate / show object detection and segmentation in image / video. Mask rcnn is competent of accomplishing good results on a range of object detection and segmentation tasks but in aerial images or video that was taken, where the object is obscure due to the environmental conditions which pose a great (challenge) to accurately / precisely detect and classify an object in image/video. so here this research paper describes/shows the implementation of Mask RCNN to locate, detect and classify the object in the image / video that was taken by Ryze Tello drone and also there further more constraints i.e. Mask requires more computational power and appropriate /sufficient amount of training samples should be given to obtain accurate results.