A ViT-based lightweight method for the UAV platform object detection tasks

Zhi Fang; ZhiZhong Xi; MengEn Xu; XiHui Fan

doi:10.1117/12.2639525

1 August 2022 A ViT-based lightweight method for the UAV platform object detection tasks

Zhi Fang, ZhiZhong Xi, MengEn Xu, XiHui Fan

Proceedings Volume 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022); 1225717 (2022) https://doi.org/10.1117/12.2639525
Event: 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 2022, Guangzhou, China

Abstract

Lightweight design is a key way to realize the engineering application of deep learning algorithms on Unmanned Aerial Vehicle (UAV) platform. Aiming at the low detection accuracy of the current real-time object detection algorithms of the UAV platform, a lightweight model based on Vision Transformer (ViT) is designed in this paper. Firstly, a small Convolutional Neural Network (CNN) is used to extract primary features for reducing the number of parameters and computation amount of ViT network in this model, and using window modeling to replace part of the global modeling. Then, a feature-level mask self-supervised training method is applied to pre-train the ViT structure, which helps to accelerate the convergence and avoid a lot of labeling work. Finally, the result compared with other UAV lightweight object detection algorithms in the visdrone2018 dataset shows that this model has higher average accuracy on ensuring real-time speed, and verifies the effectiveness and reference value of the lightweight design method proposed in this paper.

Citation Download Citation

Zhi Fang, ZhiZhong Xi, MengEn Xu, and XiHui Fan "A ViT-based lightweight method for the UAV platform object detection tasks", Proc. SPIE 12257, 4th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2022), 1225717 (1 August 2022); https://doi.org/10.1117/12.2639525

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KEYWORDS

Computer programming

Unmanned aerial vehicles

Feature extraction

Data modeling

Transformers

Visual process modeling

Detection and tracking algorithms

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