Object detection in synthetic aerial imagery using deep learning

Lalitha Dabbiru; Chris Goodin; Daniel Carruth; Jonathan Boone

doi:10.1117/12.2662426

13 June 2023 Object detection in synthetic aerial imagery using deep learning

Lalitha Dabbiru, Chris Goodin, Daniel Carruth, Jonathan Boone

Proceedings Volume 12540, Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023; 1254002 (2023) https://doi.org/10.1117/12.2662426
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

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.

Conference Presentation

Citation Download Citation

Lalitha Dabbiru, Chris Goodin, Daniel Carruth, and Jonathan Boone "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, 1254002 (13 June 2023); https://doi.org/10.1117/12.2662426

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