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SkyDataNet: An object detection algorithm with 2D gaussian loss for UAV-based aerial images

Title	SkyDataNet: An object detection algorithm with 2D gaussian loss for UAV-based aerial images
Author	Ozer, S., Begen, Ali C., Ozkanoglu, M. A.
Publication Date:	2024-01-01
Publication Place	- IEEE
Subject	2D gaussian heatmap, Single-stage detectors, UAV, Aerial imagery, Object detection
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	979-8-3503-5143-9
Record ID	47f24443-7c40-4876-b2a9-07e66102be0f
Library Location	Computer Science
Date	2024-01-01
Notes	TÜBİTAK
Sample Text	In this paper, we introduce a novel object detection algorithm based on the center-point detection. In our architecture, we introduce using two HourGlass architecture as the backbone, and we introduce using a new module to unify the predictions made after each backbone. Furthermore, since bounding boxes are in varying aspect ratios, as opposed to using a scalar Gaussian variance, we introduce using 2D variance in the Gaussian loss function to predict center-points in our network. We present the performance of our proposed improvements on three aerial datasets by comparing them to center-point based detection algorithms.
DOI	10.1109/MIPR62202.2024.00011

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Özyeğin University

SkyDataNet: An object detection algorithm with 2D gaussian loss for UAV-based aerial images

Author Ozer, S., Begen, Ali C., Ozkanoglu, M. A.

Publication Date 2024-01-01

Publication Place - IEEE

Subject 2D gaussian heatmap, Single-stage detectors, UAV, Aerial imagery, Object detection

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 979-8-3503-5143-9

Record ID 47f24443-7c40-4876-b2a9-07e66102be0f

Library Location Computer Science

Date 2024-01-01

Notes TÜBİTAK

Sample Text In this paper, we introduce a novel object detection algorithm based on the center-point detection. In our architecture, we introduce using two HourGlass architecture as the backbone, and we introduce using a new module to unify the predictions made after each backbone. Furthermore, since bounding boxes are in varying aspect ratios, as opposed to using a scalar Gaussian variance, we introduce using 2D variance in the Gaussian loss function to predict center-points in our network. We present the performance of our proposed improvements on three aerial datasets by comparing them to center-point based detection algorithms.

DOI 10.1109/MIPR62202.2024.00011