Abstract
In this paper, we explore the performance of deep residual networks in on-road object detection based on Faster R-CNN algorithm. We first optimize the setting of anchors through cluster analysis of training data. To achieve higher accuracy, we introduce a network design to combine multi-layers features. We also use a ROI spatial pyramid pooling layer to improve system performance on small objects. Experiment results show that the proposed method achieves better performance compared with baseline method.
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Acknowledgements
The work was supported in part by the National Natural Science Foundation of China under Grant numbers 61372083.
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Chen, K., Zhao, Q., Lin, Y., Zhang, J. (2017). On-Road Object Detection Based on Deep Residual Networks. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10639. Springer, Cham. https://doi.org/10.1007/978-3-319-70136-3_60
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DOI: https://doi.org/10.1007/978-3-319-70136-3_60
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