Cited by 74Advanced Neural Network ApplicationsVideo Surveillance and Tracking MethodsAutonomous Vehicle Technology and Safety

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Fast vehicle detection in UAV images

Tang, Tianyu, Deng, Zhipeng, Zhou, Shilin, Lei, Lin, Zou, Huanxin·2017·2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP)

DOI 10.1109/rsip.2017.7958795

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Abstract

Fast and accurate vehicle detection in unmanned aerial vehicle (UAV) images remains a challenge, due to its very high spatial resolution and very few annotations. Although numerous vehicle detection methods exist, most of them cannot achieve real-time detection for different scenes. Recently, deep learning algorithms has achieved fantastic detection performance in computer vision, especially regression based convolutional neural networks YOLOv2. It's good both at accuracy and speed, outperforming other state-of-the-art detection methods. This paper for the first time aims to investigate the use of YOLOv2 for vehicle detection in UAV images, as well as to explore the new method for data annotation. Our method starts with image annotation and data augmentation. CSK tracking method is used to help annotate vehicles in images captured from simple scenes. Subsequently, a regression based single convolutional neural network YOLOv2 is used to detect vehicles in UAV images. To evaluate our method, UAV video images were taken over several urban areas, and experiments were conducted on this dataset and Stanford Drone dataset. The experimental results have proven that our data preparation strategy is useful, and YOLOv2 is effective for real-time vehicle detection of UAV video images.

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Authors: Tang, T., Deng, Z., Zhou, S., Lei, L., & Zou, H.
Affiliations: National University of Defense Technology (CN)
ORCID: Tang, Tianyu
Deng, Zhipeng
Zhou, Shilin
Lei, Lin
Zou, Huanxin
Venue: 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP)
Publisher: IEEE
Published: 2017-05
Pages: 1-5
DOI: 10.1109/rsip.2017.7958795
OpenAlex: W2695028939
Cited by: 74
References: 27
Citations by year: 2026: 2 · 2025: 2 · 2024: 8 · 2023: 9 · 2022: 10 · 2021: 15 · 2020: 11 · 2019: 10 · 2018: 7
FWCI: 2.24
Type: conference paper
Language: en
Open access: closed
Locations: International Workshop on Remote Sensing with Intelligent Processing (RSIP) (publishedVersion)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
Physical Sciences › Engineering › Automotive Engineering › Autonomous Vehicle Technology and Safety
Keywords: vehicle detection, UAV imagery, YOLOv2, real-time object detection, data annotation, data augmentation, high-resolution aerial imagery, urban scenes

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Fast vehicle detection in UAV images

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