@inproceedings{c80a08a784e747fa96db81c8366d3da0,
title = "ECascade-RCNN: Enhanced Cascade RCNN for Multi-scale Object Detection in UAV Images",
abstract = "Due to the change of flight altitude and attitude of UAV, the object scale in UAV images exists difference which leads to a great challenge for object detection and has drawn wide attention. In this paper, an improved object detection network named ECascade-RCNN is proposed to deal with the multi-scale problem in object detection task for UAV images. We present an innovative Trident-FPN backbone to extract features and design a new attention mechanism to enhance the performance of the detector. Moreover, k-means algorithm is adapted to generate anchors so that the detection model can get better regression accuracy. We evaluate the proposed ECascade-R-CNN on Visdrone dataset through several ablation experiments and the results show that the ECascade-RCNN given in the paper is effective. The ECascade-RCNN is also used in the Visdrone2020 challenge and ranked 8th on the object detection track.",
keywords = "UAV images, object detection, scale variation",
author = "Qizhang Lin and Yan Ding and Hong Xu and Wenxiang Lin and Jiaxin Li and Xiaoxiao Xie",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 International Conference on Automation, Robotics and Applications, ICARA 2021 ; Conference date: 04-02-2021 Through 06-02-2021",
year = "2021",
month = feb,
day = "4",
doi = "10.1109/ICARA51699.2021.9376456",
language = "English",
series = "2021 International Conference on Automation, Robotics and Applications, ICARA 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "268--272",
booktitle = "2021 International Conference on Automation, Robotics and Applications, ICARA 2021",
address = "United States",
}