Object Detection in Synthetic Aerial Imagery Using Deep Learning

Dabbiru, L., Goodin, C., Carruth, D. W., & Boone, J. (2023). Object Detection in Synthetic Aerial Imagery Using Deep Learning. Proc. SPIE 12540, Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023. Orlando, FL, USA: SPIE. 12540. DOI:10.1117/12.2662426.

Abstract

Object detection in aerial images is a challenging task as some objects are only a few pixels wide, some objects are occluded, and some are in shade. With the cost of drones decreasing, there is a surge in the amount of aerial data, so it will be useful if models can extract valuable features from the aerial data. Convolutional neural networks (CNN) are a useful tool for object detection and machine learning applications. However, machine learning requires labeled data to train and test the CNN models. In this work, we used a simulator to automatically generate labeled synthetic aerial imagery to use in the training and testing of machine learning algorithms. The synthetic aerial data used in this work was developed using a physics-based software tool called Mississippi State University Autonomous Vehicle Simulator (MAVS). We generated a dataset of 871 aerial images of 640x480 resolution and implemented Keras-RetinaNet framework with ResNet 50 as backbone for object detection. Keras-RetinaNet is one of the popular object detection models to be used with aerial imagery. As a preliminary task, we detected buildings in the synthetic aerial imagery and our results show a high mAP (mean Average Precision) accuracy of 77.99% using the state-of-the-art RetinaNet model.