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• W4320925627 abstract "Aerial object detection is a key to many functionalities like animal population estimation, pedestrian counting, security systems and many more. Traditional methods made use of machine learning algorithms that involved hand-made features. At present, deep learning models have surpassed the conventional machine learning models. Some of the major problems faced in aerial object detection are angle shift and the small sizes of objects in aerial images. This paper discusses the developments in aerial object detection. It discusses the various approaches presented by researchers, the different datasets available today for the task, and some of the standard evaluation and metrics followed to evaluate models." @default.
• W4320925627 created "2023-02-16" @default.
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• W4320925627 date "2023-01-01" @default.
• W4320925627 modified "2023-09-23" @default.
• W4320925627 title "Aerial Object Detection Using Deep Learning: A Review" @default.
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• W4320925627 doi "https://doi.org/10.1007/978-981-19-7346-8_8" @default.
• W4320925627 hasPublicationYear "2023" @default.
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