Deep neural network based approach for robust aerial surveillance (original) (raw)

2021

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 propose...

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