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butterflydetector

FAIR level 2

fair level

This software has some basic information to help you get it running, but it may be an uphill struggle.

butterflydetector

Developed by

https://www.epfl.ch/labs/vita/wp-content/uploads/2020/11/Perceiving_traffic_from_aerial_images.pdf

Current state-of-the-art object detectors have achieved high performance when applied to images captured by standard front facing cameras. When applied to high-resolution aerial images captured from a drone or UAV stand-point, they fail to generalize to the wide range of objects' scales. In order to address this limitation, we propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We extend the concept of fields and introduce butterfly fields, a type of composite field that describes the spatial information of output features as well as the scale of the detected object. To overcome occlusion and viewing angle variations that can hinder the localization process, we employ a voting mechanism between related butterfly vectors pointing to the object center. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.

Python

Library

https://spdx.org/licenses/AGPL-3.0.html

Description

Current state-of-the-art object detectors have achieved high performance when applied to images captured by standard front facing cameras. When applied to high-resolution aerial images captured from a drone or UAV stand-point, they fail to generalize to the wide range of objects' scales. In order to address this limitation, we propose an object detection method called Butterfly Detector that is tailored to detect objects in aerial images. We extend the concept of fields and introduce butterfly fields, a type of composite field that describes the spatial information of output features as well as the scale of the detected object. To overcome occlusion and viewing angle variations that can hinder the localization process, we employ a voting mechanism between related butterfly vectors pointing to the object center. We evaluate our Butterfly Detector on two publicly available UAV datasets (UAVDT and VisDrone2019) and show that it outperforms previous state-of-the-art methods while remaining real-time.

Date Created

2020-03-20T00:00:00.000Z

Date Updated

2023-04-20T00:00:00.000Z

Feature List

Application Category

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