Abstract
Vehicle detection in aerial images has been applied in many fields and attracted more and more scholars’ attention. In the task, the objects are multi-directional and arranged densely, the background information is complex, and the scale of the object is different. To achieve better detection performance, an improved detection model BFR-RetinaNet is proposed, which is based on the single-stage object detection model RetinaNet. BFR-RetinaNet optimizes vehicle positioning by adding a directional anchor box regression branch. Simultaneously, the model introduces a balanced feature pyramid structure to enhance the extraction of features and reduce the interference of complex backgrounds. The experimental results on the aerial dataset show that the precision, recall, and average precision of the proposed model have been improved to varying degrees. It achieves 86.2% Precision, 98.4% Recall, and 90.8% Average Precision (AP), which is 7.96, 0.45, and 0.58 points higher than R-RetinaNet.
This work is partially supported by the Open Research Project of the State Key Laboratory of Industrial Control Technology (No. ICT2021B10), the Natural Science Foundation of Hu-nan Province (2021JJ30456), the Open Fund of Science and Technology on Parallel and Distributed Processing Laboratory (WDZC20205500119), the Hunan Provincial Science and Technology Department High-tech Industry Science and Technology Innovation Lead-ing Project (2020GK2009) and the Scientific and Technological Progress and Innovation Program of the Transportation Department of Hunan Province (201927).
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Zhang, J.I.N., Luo, M., Sun, C., Qu, P. (2022). BFR-RetinaNet: An Improved RetinaNet Model for Vehicle Detection in Aerial Images. In: Lai, Y., Wang, T., Jiang, M., Xu, G., Liang, W., Castiglione, A. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2021. Lecture Notes in Computer Science(), vol 13155. Springer, Cham. https://doi.org/10.1007/978-3-030-95384-3_2
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