Instance segmentation based precise object detection in UAV Images using Mask R-CNN

Abstract

Object detection plays a vital role in remote-sensing dataset which trains the image or things and helps in classifying the images into its classes. Instance segmentation is the avant-garde technique used for object detection in Deep Learning. There are number of instance segmentation models which can produce significant results. Object detection, segmentation and RGB analysis in images taken from Unmanned Aerial Vehicle (UAV) are difficult with desired level of performance. Instance segmentation is a powerful method that extracts each object and its location with the predicted label for pixels in the input image. In this paper, a study has been carried out on implementation of Mask R-CNN for instance segmentation with different optimization algorithms to obtain a more accurate result for UAV images. The training has been carried out with Mask R-CNN for object detection using ResNet50 and ResNet101 as backbone. After extensive experiments, it has been observed that the optimization algorithm plays the vital role on the overall computational process and can improve the accuracy level with reduction in the training/ validation loss. The experiment has been conducted on the publicly available UAV datasets. The paper further presents the results in terms of different performance parameters.