Detection of Strategic Targets of Interest in Satellite Images using YOLO

This paper details about training a convolutional neural network for object detection and classification of a custom generated dataset from google earth satellite images. The objects of interest in satellite images are strategic targets such as nuclear power plants and oil refineries. The deep learning network is YOLO version 3 which has shown significant improvement in detecting smaller objects. YOLO v3 is three times faster than SSD and its AP metric for COCO dataset is on par with SSD. Hence YOLO v3 is a faster detector in comparison to SSD. But AP
_0.75
is low when compared with RetinaNet due to higher localization error. On the other hand, the simpler network of YOLO, Tiny YOLO v3 takes lesser detection time. The objective of experimentation is to evaluate the performance of YOLO v3 and Tiny YOLO v3 for objects from satellite imagery. Google earth satellite images consumes less time and provides cost effective solution rather than acquiring the overhead images through unmanned aerial vehicles (UAVs) and drones. The objects size varies from 120 pixels to 1250 pixels. The experimental results demonstrate its detection capability, metrics results. GeForce RTX GPU was used for training the network.