On-road object detection using deep neural network

Industrialization of transportation system has derived serious accidents that resulted in thousands of deaths. To solve the problem, vision based object detection for autonomous vehicle and advanced driver assistance system has been researched. In this study, we provide experimentations of object detection and localization in on-road environment using deep neural network. We compared the detection accuracy among object classes and analyzed the recognition results with fine-tuned Single shot multibox detector on KITTI dataset. This work improves the performance of original detection model by increasing precision of overall detection about 6%, especially about 10% in pedestrian and cyclist.

[1]  G. Sottile,et al.  Characterization and performance of the ASIC (CITIROC) front-end of the ASTRI camera , 2015, 1506.00264.

[2]  Xiang Zhang,et al.  OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks , 2013, ICLR.

[3]  Andreas Geiger,et al.  Vision meets robotics: The KITTI dataset , 2013, Int. J. Robotics Res..

[4]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Paul A. Viola,et al.  Robust Real-Time Face Detection , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[6]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[7]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[9]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[10]  Sebastian Thrun,et al.  Towards fully autonomous driving: Systems and algorithms , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).