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Attention based Object Classification for Drone Imagery | IEEE Conference Publication | IEEE Xplore

Attention based Object Classification for Drone Imagery


Abstract:

This paper shows how to make the drone imagery for surveillance or tracking the object in the ground. To detect or classify objects on the ground, convolutional neural ne...Show More

Abstract:

This paper shows how to make the drone imagery for surveillance or tracking the object in the ground. To detect or classify objects on the ground, convolutional neural networks was adopted and compared with some existed methods and the proposed attention blocks in it. The objects on the ground from the drone images are relatively very small and diversity of the appearance from its perspective projections. This is mainly due to the arbitrary viewpoints from the bird eye views. Furthermore, the distance from its viewpoint in the sky is quite much changeable so that the image of the object is too diverse in appearance and its size. However, the drone is so useful to see widely while navigating in the sky. It is much more attentive to use for real application. Here, some proposed target objects are mainly located in the ground, like static and dynamic objects such as street lamps or trees, vehicles, trucks and pedestrians. These works were done for the national projects to establish the general AI services in Korea recently. For the experiments such as buildup the ground truth of target objects after taken in regulated distance and viewing angles and performed to detect exactly objects in an arbitrary image. For experiments of detection and classification of five categories of objects, attention based CNN architecture was adopted and compared comprehensively with the existed networks like MobileNet, VGG16, SqueezeNet, and ResNet. The experimental results outperformed for the archived drone image dataset with 87.12% in precision. The architecture shows almost 3 times faster with respect to VGG16 or 2 times faster than MobileNet in the speed but a half slimer and twice thicker respectively in the number of parameters. Thus, the Attention Block is useful while a drone navigates through a certain route according to the ground location regardless of the appearance and size of the target region in image.
Date of Conference: 13-16 October 2021
Date Added to IEEE Xplore: 10 November 2021
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Conference Location: Toronto, ON, Canada

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