Evaluating the influence of backbone network architectures for object detection in  aerial images

Khang Nguyen

doi:10.15625/2525-2518/17595

Author affiliations

Authors

Khang Nguyen University of Information Technology, 1 Han Thuyen Street, Quarter 6, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Viet Nam;
Vietnam National University, km 20 Ha Noi Highway, Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Viet Nam

DOI:

https://doi.org/10.15625/2525-2518/17595

Keywords:

aerial images, object detection, vehicle detection, convolutional neural networks

Abstract

Drones are increasingly being used in surveillance, agriculture, and delivery tasks. However, the real-life application of images collected from drones in urban manage- ment in Vietnam is still limited. Although drone images have many advantages thanks to the flexibility of the latest devices, there are still new challenges, such as top-down views, small objects, arbitrary directions, and class imbalance. In this paper, we conduct research, survey, and evaluate the performance of CNN-based network architectures on object detection in aerial images. Experiments were conducted on seven deep learning network architectures: VGG, ResNet, ResNext, Res2Net, ResNeSt, HRNet, and RegNet to bring objective judgments and conclusions based on experiments, contributing to the development of solutions for applications of determining the status of urban traffic in Vietnam.

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