Validation of object detection in UAV-based images using synthetic data

Eung-Joo Lee; Damon M. Conover; Shuvra S. Bhattacharyya; Heesung Kwon; Jason Hill; Kenneth Evensen

doi:10.1117/12.2586860

12 April 2021 Validation of object detection in UAV-based images using synthetic data

Eung-Joo Lee, Damon M. Conover, Shuvra S. Bhattacharyya, Heesung Kwon, Jason Hill, Kenneth Evensen

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117462A (2021) https://doi.org/10.1117/12.2586860
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Object detection is increasingly used onboard Unmanned Aerial Vehicles (UAV) for various applications; however, the machine learning (ML) models for UAV-based detection are often validated using data curated for tasks unrelated to the UAV application. This is a concern because training neural networks on large-scale benchmarks have shown excellent capability in generic object detection tasks, yet conventional training approaches can lead to large inference errors for UAV-based images. Such errors arise due to differences in imaging conditions between images from UAVs and images in training. To overcome this problem, we characterize boundary conditions of ML models, beyond which the models exhibit rapid degradation in detection accuracy. Our work is focused on understanding the impact of different UAV-based imaging conditions on detection performance by using synthetic data generated using a game engine. Properties of the game engine are exploited to populate the synthetic datasets with realistic and annotated images. Specifically, it enables the fine control of various parameters, such as camera position, view angle, illumination conditions, and object pose. Using the synthetic datasets, we analyze detection accuracy in different imaging conditions as a function of the above parameters. We use three well-known neural network models with different model complexity in our work. In our experiment, we observe and quantify the following: 1) how detection accuracy drops as the camera moves toward the nadir-view region; 2) how detection accuracy varies depending on different object poses, and 3) the degree to which the robustness of the models changes as illumination conditions vary.

Conference Presentation

Citation Download Citation

Eung-Joo Lee, Damon M. Conover, Shuvra S. Bhattacharyya, Heesung Kwon, Jason Hill, and Kenneth Evensen "Validation of object detection in UAV-based images using synthetic data", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117462A (12 April 2021); https://doi.org/10.1117/12.2586860

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