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Multi-scale Feature Fusion UAV Image Object Detection Method Based on Dilated Convolution and Attention Mechanism

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Mengmeng ChangAuthors Info & Claims

ICIT '20: Proceedings of the 2020 8th International Conference on Information Technology: IoT and Smart City

Pages 125 - 132

https://doi.org/10.1145/3446999.3447023

Published: 09 April 2021 Publication History

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Abstract

Due to the influence of the shooting angle of view and the flight height, the images taken by UAV often have complex backgrounds and contain a large number of small and unevenly distributed objects. In order to solve the problem that it is difficult to accurately locate and recognize small objects in UAV images under complex backgrounds, this paper proposes an multi-scale feature fusion algorithm D-A-FS SSD (Dilated-Attention-Feature Fusion SSD) based on the combination of dilated convolution and attention mechanism. In the process of feature extraction, the receptive field of the feature is expanded through the dilated convolution, which improves the network's feature expression of object distribution and scale difference information. And a attention network is used in our method to effectively suppresse the background information. In the multi-scale detection stage, our method fuses the low-level feature map responsible for detecting small objects with the high-level feature map which have much higher semantic information to improve the recognition accuracy of small objects. Experimental results show that our method effectively improves the accuracy of UAV image object detection.

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Cited By

Larrat MSales C(2025)Classification of Flying Drones Using Millimeter-Wave Radar: Comparative Analysis of Algorithms Under Noisy ConditionsSensors10.3390/s2503072125:3(721)Online publication date: 24-Jan-2025
https://doi.org/10.3390/s25030721
Yu CShin Y(2024)MCG-RTDETR: Multi-Convolution and Context-Guided Network with Cascaded Group Attention for Object Detection in Unmanned Aerial Vehicle ImageryRemote Sensing10.3390/rs1617316916:17(3169)Online publication date: 27-Aug-2024
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ICIT '20: Proceedings of the 2020 8th International Conference on Information Technology: IoT and Smart City

December 2020

266 pages

ISBN:9781450388559

DOI:10.1145/3446999

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 09 April 2021

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ICIT 2020

ICIT 2020: IoT and Smart City

December 25 - 27, 2020

Xi'an, China

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Cited By

Larrat MSales C(2025)Classification of Flying Drones Using Millimeter-Wave Radar: Comparative Analysis of Algorithms Under Noisy ConditionsSensors10.3390/s2503072125:3(721)Online publication date: 24-Jan-2025
https://doi.org/10.3390/s25030721
Yu CShin Y(2024)MCG-RTDETR: Multi-Convolution and Context-Guided Network with Cascaded Group Attention for Object Detection in Unmanned Aerial Vehicle ImageryRemote Sensing10.3390/rs1617316916:17(3169)Online publication date: 27-Aug-2024
https://doi.org/10.3390/rs16173169
Jiang BLi NCui XZhang QZhang HLi ZLiu W(2024)Research on facial expression recognition algorithm based on improved MobileNetV3EURASIP Journal on Image and Video Processing10.1186/s13640-024-00638-z2024:1Online publication date: 22-Aug-2024
https://doi.org/10.1186/s13640-024-00638-z
Zhou XLi LChen B(2024)LENet: Lightweight and Effective Detector for Aerial ObjectUnmanned Systems10.1142/S230138502550038412:06(1105-1121)Online publication date: 8-May-2024
https://doi.org/10.1142/S2301385025500384
Liu JLiu XChen QNiu S(2023)A Traffic Parameter Extraction Model Using Small Vehicle Detection and Tracking in Low-Brightness Aerial ImagesSustainability10.3390/su1511850515:11(8505)Online publication date: 24-May-2023
https://doi.org/10.3390/su15118505
Wang XHe NHong CWang QChen M(2023)Improved YOLOX-X based UAV aerial photography object detection algorithmImage and Vision Computing10.1016/j.imavis.2023.104697135(104697)Online publication date: Jul-2023
https://doi.org/10.1016/j.imavis.2023.104697
Wang QGuo CZhao ZWang YHu LZhai Y(2023)SIRN: An iterative reasoning network for transmission lines based on scene prior knowledgeEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106656125(106656)Online publication date: Oct-2023
https://doi.org/10.1016/j.engappai.2023.106656
Wu XLi WHong DTao RDu Q(2022)Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A surveyIEEE Geoscience and Remote Sensing Magazine10.1109/MGRS.2021.311513710:1(91-124)Online publication date: Mar-2022
https://doi.org/10.1109/MGRS.2021.3115137
Han YLiu HWang YLiu C(2022)A Comprehensive Review for Typical Applications Based Upon Unmanned Aerial Vehicle PlatformIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2022.321656415(9654-9666)Online publication date: 2022
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Zhou YLing J(2022)Improved YOLOv5-based UAV Target Detection under Complex Background2022 2nd International Conference on Computational Modeling, Simulation and Data Analysis (CMSDA)10.1109/CMSDA58069.2022.00046(217-221)Online publication date: 2-Dec-2022
https://doi.org/10.1109/CMSDA58069.2022.00046
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