Multi-Class Object Detection from Aerial Images Using Mask R-CNN

Object detection, though gaining popularity, has largely been limited to detection from the ground or from satellite imagery. Aerial images, where the target may be obfuscated from the environmental conditions, angle-of-attack, and zoom level, pose a more significant challenge to correctly detect targets in. This paper describes the implementation of a regional convolutional neural network to locate and classify objects across several categories in complex, aerial images. Our current results show promise in detecting and classifying objects. Further adjustments to the network and data input should increase the localization and classification accuracies.