Abstract
In this paper, an object detection model based on MFF-RPN and Multi-scale CNN is proposed. Firstly, the region proposal network based on multi-level feature fusion (MFF-RPN) is presented to extract the candidate proposals. Secondly, a convolutional neural network (CNN) with different scale convolution kernels is conducted to extract features adaptively. Finally, multi-task loss is employed to establish a complex mapping between image object features and object detection mode. The experimental results prove that the proposed algorithm gets better classification performance and higher detection accuracy.
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Acknowledgements
We would like to thank the supports by Chongqing Nature Science Foundation for Fundamental science and frontier technologies (cstc2015jcyjB0569), China Central Universities Foundation (106112016CDJZR175511).
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Zhou, J., Zheng, H., Yin, H., Chai, Y. (2018). Object Detection from Images Based on MFF-RPN and Multi-scale CNN. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2017 Chinese Intelligent Systems Conference. CISC 2017. Lecture Notes in Electrical Engineering, vol 460. Springer, Singapore. https://doi.org/10.1007/978-981-10-6499-9_33
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DOI: https://doi.org/10.1007/978-981-10-6499-9_33
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