Deep neural network based approach for robust aerial surveillance

Ruixu Liu; Theus H. Aspiras; Vijayan K. Asari

doi:10.1117/12.2591194

12 April 2021 Deep neural network based approach for robust aerial surveillance

Ruixu Liu, Theus H. Aspiras, Vijayan K. Asari

Proceedings Volume 11735, Pattern Recognition and Tracking XXXII; 117350B (2021) https://doi.org/10.1117/12.2591194
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Aerial object detection is one of the most important applications in computer vision. We propose a deep learning strategy for detection and classification of objects on the pipeline right of ways by analyzing aerial images captured by flying aircrafts or drones. Due to the limitation of sufficient aerial datasets for accurately training the deep learning systems, it is necessary to create an efficient methodology for object data augmentation of the training dataset to achieve robust performance in various environmental conditions. Another limitation is the computing hardware that could be installed on the aircraft, especially when it is a drone. Hence a balance between the effectiveness and efficiency of object detector needs to be considered. We propose an efficient weighted IOU NMS (intersection over union non-maxima suppression) method to speed up the post-processing time that satisfies the onboard processing requirement. Weighted IOU NMS utilizes confidence scores of all proposed bounding boxes to regenerate a mean box in parallel. It processes the bounding box score at the same instant without removing the bounding box or decreasing the bounding box score. We perform both quantitative and qualitative evaluations of our network architecture on multiple aerial datasets. The experimental results show that our proposed framework achieves better accuracy than the state-of-the-art methods for aerial object detection in various environmental conditions.

Conference Presentation

Citation Download Citation

Ruixu Liu, Theus H. Aspiras, and Vijayan K. Asari "Deep neural network based approach for robust aerial surveillance", Proc. SPIE 11735, Pattern Recognition and Tracking XXXII, 117350B (12 April 2021); https://doi.org/10.1117/12.2591194

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