Abstract
Pedestrian detection algorithms based on deep learning often rely on high-performance image processing platforms, and it does not adapt to high portability requirements of practical engineering. In order to solve the problem, an improved approach is proposed, which can be applied to embedded platforms. Firstly, reinforcing dense connectivity is adopted to connect the multiple layer features. Then the k-means++ algorithm is used to cluster the pedestrian targets, and the method of eliminating duplicate detection is optimized. Finally, the detection model is obtained through multi-scale training. Experiment results show that Dense-YOLOv3-tiny can detect pedestrian on UAV imagery in real time and exhibit higher precision and recall rate than the YOLOv3-tiny model in the embedded platforms.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China (61571346). The research is also supported by the Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University.
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Yang, Y., Guo, B., Li, C., Zhi, Y. (2020). An Improved YOLOv3 Algorithm for Pedestrian Detection on UAV Imagery. In: Pan, JS., Lin, JW., Liang, Y., Chu, SC. (eds) Genetic and Evolutionary Computing. ICGEC 2019. Advances in Intelligent Systems and Computing, vol 1107. Springer, Singapore. https://doi.org/10.1007/978-981-15-3308-2_29
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DOI: https://doi.org/10.1007/978-981-15-3308-2_29
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