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REAL TIME PEDESTRIAN ALERT SYSTEM FOR VEHICLES

Yıl 2020, Cilt: 21 Sayı: 3, 446 - 453, 30.09.2020
https://doi.org/10.18038/estubtda.629701

Öz

In this study, we have developed a pre-collision alert
system for vehicles in terms of detection pedestrians in road. The system is
consisting from deep learning models and transfer learning methodologies. For
this purpose, pre-trained convolutional models was considered to detect
pedestrian and road.  Finally, the
segmented road mask and pedestrian mask was utilized to reveal the intersection
of these two masks. The system generates an alert if the number of pixels is
higher than predefined threshold value. By considering the visual results, the
proposed system gives valuable detection results to avoid collision.

Kaynakça

  • [1] 'Ford, Https://Www.Ford.Com/Technology/Driver-Assist-Technology/Pre-Collision-Assist/, Accessed 09.27.2019'.
  • [2] 'Qualcom, Https://Www.Qualcomm.Com/Videos/Honda-V2p-Overview, Accessed 27.09.2019'.
  • [3] 'Bosch, Https://St-Tpp.Resource.Bosch.Com/Media/Technology_Partner_Programm/10_Public/ Application_Notes/Iss_Ipp_Application_Note_210x280_Final_Lowres_Hyperlinks.Pdf, Accessed 09.27.2019'.
  • [4] Said YF. and Barr M. Pedestrian Detection for Advanced Driver Assistance Systems Using Deep Learning Algorithms, International Journal of Computer Science and Network Security, 2019; 19, (9), p. 10.
  • [5] Xu C, Wang G, Yan S, Yu J, Zhang B, Dai S, Li Y and Xu L. Fast Vehicle and Pedestrian Detection Using Improved Mask R-Cnn. Mathematical Problems in Engineering, 2020.
  • [6] Zhang S, Abdel-Aty M, Yuan J and Li P. Prediction of Pedestrian Crossing Intentions at Intersections Based on Long Short-Term Memory Recurrent Neural Network. Transportation Research Record, 2020; p. 0361198120912422.
  • [7] Fuentes A, Jun I, Yoon S and Dong S. Pedestrian Detection for Driving Assistance Systems Based on Faster-Rcnn, in, International Symposium on Information Technology Convergence Isitc, 2016.
  • [8] Long J, Shelhamer E and Darrell T. Fully Convolutional Networks for Semantic Segmentation', in, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
  • [9] Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, and Adam H.J.a.p.a. Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications, 2017.
  • [10] Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y and Berg AC. Ssd: Single Shot Multibox Detector, in, European Conference on Computer Vision, Springer, 2016.
  • [11] Tensorflow, Https://Www.Tensorflow.Org/, Accessed 09.27.2019.
  • [12] Simonyan K and Zisserman A.J.a.p.a., Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014.
  • [13] He K, Zhang X, Ren S and Sun J. Deep Residual Learning for Image Recognition, in, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
  • [14] 'Medium, Https://Medium.Com/@Jonathan_Hui/Object-Detection-Speed-and-Accuracy-Comparison-Faster-R-Cnn-R-Fcn-Ssd-and-Yolo-5425656ae359, Accessed 10.03.2019'.
Yıl 2020, Cilt: 21 Sayı: 3, 446 - 453, 30.09.2020
https://doi.org/10.18038/estubtda.629701

Öz

Kaynakça

  • [1] 'Ford, Https://Www.Ford.Com/Technology/Driver-Assist-Technology/Pre-Collision-Assist/, Accessed 09.27.2019'.
  • [2] 'Qualcom, Https://Www.Qualcomm.Com/Videos/Honda-V2p-Overview, Accessed 27.09.2019'.
  • [3] 'Bosch, Https://St-Tpp.Resource.Bosch.Com/Media/Technology_Partner_Programm/10_Public/ Application_Notes/Iss_Ipp_Application_Note_210x280_Final_Lowres_Hyperlinks.Pdf, Accessed 09.27.2019'.
  • [4] Said YF. and Barr M. Pedestrian Detection for Advanced Driver Assistance Systems Using Deep Learning Algorithms, International Journal of Computer Science and Network Security, 2019; 19, (9), p. 10.
  • [5] Xu C, Wang G, Yan S, Yu J, Zhang B, Dai S, Li Y and Xu L. Fast Vehicle and Pedestrian Detection Using Improved Mask R-Cnn. Mathematical Problems in Engineering, 2020.
  • [6] Zhang S, Abdel-Aty M, Yuan J and Li P. Prediction of Pedestrian Crossing Intentions at Intersections Based on Long Short-Term Memory Recurrent Neural Network. Transportation Research Record, 2020; p. 0361198120912422.
  • [7] Fuentes A, Jun I, Yoon S and Dong S. Pedestrian Detection for Driving Assistance Systems Based on Faster-Rcnn, in, International Symposium on Information Technology Convergence Isitc, 2016.
  • [8] Long J, Shelhamer E and Darrell T. Fully Convolutional Networks for Semantic Segmentation', in, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015.
  • [9] Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, and Adam H.J.a.p.a. Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications, 2017.
  • [10] Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y and Berg AC. Ssd: Single Shot Multibox Detector, in, European Conference on Computer Vision, Springer, 2016.
  • [11] Tensorflow, Https://Www.Tensorflow.Org/, Accessed 09.27.2019.
  • [12] Simonyan K and Zisserman A.J.a.p.a., Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014.
  • [13] He K, Zhang X, Ren S and Sun J. Deep Residual Learning for Image Recognition, in, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016.
  • [14] 'Medium, Https://Medium.Com/@Jonathan_Hui/Object-Detection-Speed-and-Accuracy-Comparison-Faster-R-Cnn-R-Fcn-Ssd-and-Yolo-5425656ae359, Accessed 10.03.2019'.
Toplam 14 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Şahin Işık 0000-0003-1768-7104

Göksu Vatansever Bu kişi benim 0000-0002-5083-0526

Yıldıran Anagun 0000-0003-2737-2720

Mehmet Çelikhan Bu kişi benim 0000-0001-6965-1862

Kemal Özkan 0000-0003-2252-2128

Yayımlanma Tarihi 30 Eylül 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 21 Sayı: 3

Kaynak Göster

AMA Işık Ş, Vatansever G, Anagun Y, Çelikhan M, Özkan K. REAL TIME PEDESTRIAN ALERT SYSTEM FOR VEHICLES. Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering. Eylül 2020;21(3):446-453. doi:10.18038/estubtda.629701