Rear-Approaching Vehicle Detection using Frame Similarity base on Faster R-CNN

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    In this paper, we propose a new algorithm to detect rear-approaching vehicle using frame structure similarity based on deep learning algorithm for use in agricultural machinery systems. The commonly used deep learning models well detect various types of vehicles and detect the shapes of vehicles from various camera angles. However, since the vehicle detection system for agricultural machinery needs to detect only a vehicle approaching from the rear, when a general deep learning model is used, a false positive is generated by a vehicle running on the opposite side (passing vehicle). In this paper, first, we use Faster R-CNN model that shows excellent accuracy rate in deep learning for vehicle detection. Second, we proposed an algorithm that uses the structural similarity and the root mean square comparison method for the region of interest(vehicles area) which is detected by Faster R-CNN between the coming vehicle and the passing vehicle. Experimental results show that the proposed method has a detection rate of 98.2% and reduced the false positive values, which is superior to general deep learning method.

     

     

  • Keywords


    faster r-cnn; vehicle detection; structural similarity index; deep learning; agricultural machine.

  • References


      [1] Chen C, Rear Approaching Vehicle Detection with Microphone, Bachelor’s Thesis, Halmstad University, (2013).

      [2] Ananthanarayanan VK, Audio Based Detection of Rear Approaching Vehicle on a Bicycle, Graduate School Thesis, Rutgers University, (2012).

      [3] Chen CT and Chen YS, Real-time approaching vehicle detection in blind-spot area, 12th Internal IEEE Conference on intelligent Transportation Systems, 2009.

      [4] Kang HW Baek JW, and Jeong YS, Real-Time Side-Rear Vehicle Detection Algorithm for Blind Spot Warning Systems, KIISE Transactions on Computing Practice, V.23, No.7, (2018), pp. 408-416.

      [5] Dobahue J, Girshick R, Darrell T, and Malik J, Rich feature hierarchies for accurate object detection and semantic segmentation, IEEE Internal Conference on Computer Vision and Pattern Recognition, (2014), pp:580-587.

      [6] Ross Girshick, Faster-RCNN, 2015 IEEE International Conference on Computer Vision, (2015), pp:1440-1448.

      [7] Ren S, He K, Gisshick R, and Sun J, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks, IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 38, No. 6, (2017), pp. 1137-1149.

      [8] Quanfu F, Lisa B, and Hohn S, A Closer Look at Faster R-CNN for Vehicle Detection, 2016 Intelligent Vehicle Symposium, (2016), pp:124-129.

      [9] Hsu SC, Huang CL, Chuang CH, Vehicle Detection using simplified Fast R-CNN, International Workshop on Advanced Image Technology, (2018).

      [10] Kim HS and Park JS, intensity-based efficient Video Quality Assessment for Variable bitrate Streaming, Korean Institute of Next Generation Computing, Vol.11, No.5, (2015), pp.63-71.


 

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Article ID: 26979
 
DOI: 10.14419/ijet.v7i4.44.26979




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