Ensemble Deep Learning Using Faster R-CNN and Genetic Algorithm for Vehicle Detection in UAV Images

被引：14

作者：

Ghasemi Darehnaei, Zeinab ^{[1
]}

Rastegar Fatemi, Seyed Mohammad Jalal ^{[1
]}

Mirhassani, Seyed Mostafa ^{[2
]}

Fouladian, Majid ^{[1
]}

机构：

[1] Islamic Azad Univ, Dept Elect Engn, Coll Engn, Saveh Branch, Saveh, Iran

[2] Islamic Azad Univ, Dept Elect Engn, Saveh Branch, Shahrood, Iran

来源：

IETE JOURNAL OF RESEARCH | 2023年 / 69卷 / 08期

关键词：

Deep learning; ensemble learning; faster R-CNN; genetic algorithm; transfer learning; vehicle detection; OBJECT DETECTION; NETWORKS;

D O I：

10.1080/03772063.2021.1962418

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, an ensemble deep transfer learning (EDTL) based on Faster R-CNN is introduced for the vehicle detection in UAV images. We perform a weighted-averaging ensemble transfer learning comprising six base learners using a ResNet50 that have already pre-trained on ImageNet dataset. The weights of the six base learners as well as the final decision threshold are adaptively optimized via genetic algorithm, to maximize the total accuracy, precision, and recall. Simulation results on AU-AIR dataset demonstrate the superiority of the EDTL against the existing techniques, in terms of the total accuracy, and the trade-off between precision and recall.

引用

页码：5102 / 5111

页数：10

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