Abstract
Security surveillance is widely used in daily life. For nighttime or complicated monitoring environments, this article proposes an infrared pedestrian monitoring based on YOLOv3. In the original YOLOv3 network structure, two aspects of optimization were made. One was to optimize the scale in the residual structure, and the rich features of the deconvolution layer were added to the original residual structure. The other was to use the DenseNet network to enhance the features. The optimization of fusion ability and delivery ability effectively improves the detection ability for small targets, and the pedestrian detection performance based on infrared images. After comparative testing, compared with YOLOv3, the overall mean average precision is improved by 4.39% to 78.86%.
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Acknowledgements
This paper was supported by the Fundamental Research Funds for the Local Universities of Hei longjiang Province in 2018 (Grant No. 2018-KYYWF-1189) and Shanghai Aerospace Science and Technology Innovation Fund (Grand No. SAST2017-104).
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Shi, J., Zhang, G., Yuan, J., Zhang, Y. (2020). Improved YOLOv3 Infrared Image Pedestrian Detection Algorithm. In: Zeng, J., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2020. Communications in Computer and Information Science, vol 1257. Springer, Singapore. https://doi.org/10.1007/978-981-15-7981-3_37
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DOI: https://doi.org/10.1007/978-981-15-7981-3_37
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