On-Road Object Detection Based on Deep Residual Networks

  • Kang Chen
  • Qi Zhao
  • Yaorong Lin
  • Jun Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10639)


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.


Object detection CNN Deep learning ResNet 



The work was supported in part by the National Natural Science Foundation of China under Grant numbers 61372083.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.School of Electronic and Information EngineeringSouth China University of TechnologyGuangzhouChina

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