Abstract
Real-time object detection plays a significant role in the field of computer vision. Advanced object detection networks combine with the distribution characteristics in the image, while exposing detecting small targets as a bottleneck. In this paper, a novel network YOLO-light, can real-time detect and accurately detect objects in embedded system or portable devices. Firstly, in order to gaining better priori boxes, the clustering analysis is applied to pre-processing. Secondly, inspired from the multi-scale connection in the Feature Pyramid Networks (FPN) algorithm, YOLO-light enables multi-view and integrate features of various scales. With this design, YOLO-light can end-to-end training with low latency and higher average precision. The experiments testify that YOLO-light algorithm reveals satisfactory performance both in speed and accuracy.
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Acknowledgment
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. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
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Wei, Z., Huang, Z., Guo, B., Li, C., Wang, G. (2020). Lightweight Real-Time Object Detection Based on Deep Learning. 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_39
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DOI: https://doi.org/10.1007/978-981-15-3308-2_39
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