Performance evaluation of multimodal deep learning: object identification using UAV dataset

Mingju He; Myron Hohil; Thomas LaPeruta; Kerolos Nashed; Victor Lawrence; Yu-Dong Yao

doi:10.1117/12.2587825

12 April 2021 Performance evaluation of multimodal deep learning: object identification using UAV dataset

Mingju He, Myron Hohil, Thomas LaPeruta, Kerolos Nashed, Victor Lawrence, Yu-Dong Yao

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117462B (2021) https://doi.org/10.1117/12.2587825
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Object identification or classification has found many applications, ranging from civilian to defense application scenarios and there is a rising need for a both effective and efficient identification approach. One of the common methods for this task is using a neural network. However, it could be very difficult for such a network to obtain accurate answers due to complex environments, especially when the data is of single modality. In this paper, we attempt to build a combined deep learning model which takes two distinct data modalities to help us achieve high accuracy multimodal classification systems. An experiment is conducted on Multimodal Unmanned Aerial Vehicle Dataset for Low Altitude Traffic Surveillance (AU-AIR) using both visual and sensor data. We compare our results between a model trained with visual data only and another combined model trained with both visual and sensor data. Improved object classification performance is observed when the multimodal method is applied.

Conference Presentation

Citation Download Citation

Mingju He, Myron Hohil, Thomas LaPeruta, Kerolos Nashed, Victor Lawrence, and Yu-Dong Yao "Performance evaluation of multimodal deep learning: object identification using UAV dataset", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117462B (12 April 2021); https://doi.org/10.1117/12.2587825

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