Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles

Gozde YOLCU, (Sakarya Üniversitesi, Bilgisayar ve Bilişim Bilimleri Fakültesi, Yazılım Mühendisliği Bölümü, Sakarya, Türkiye)
İsmail ÖZTEL (Sakarya Üniversitesi, Bilgisayar ve Bilişim Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü, Sakarya, Türkiye)
Sakarya University Journal of Computer and Information Sciences (Online)
6 0
Yıl: 2023 Cilt: 6 Sayı: 1 Sayfa Aralığı: 22 - 31 Metin Dili: İngilizce DOI: 10.35377/saucis...1170902 İndeks Tarihi: 09-05-2023

Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles

Öz:
Correctly determining the driving area and pedestrians is crucial for intelligent vehicles to reduce fatal road accidents risk. But these are challenging tasks in the computer vision field. Various weather, road conditions, etc., make them difficult. This paper presents a vision-based road segmentation and pedestrian detection system. First, the roads are segmented using a deep learning based consecutive triple filter size (CTFS) approach. Then, pedestrians on the segmented roads are detected using the transfer learning approach. The CTFS approach can create feature maps for small and big features. The proposed system is a reliable, low-cost road segmentation and pedestrian detection system for intelligent vehicles.
Anahtar Kelime: Pedestrian detection road segmentation convolutional neural networks intelligent vehicles Transfer Learning

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • [1] K.-W. Chiang and Y.-W. Huang, “An intelligent navigator for seamless INS/GPS integrated land vehicle navigation applications,” Appl Soft Comput, vol. 8, no. 1, pp. 722–733, Jan. 2008, doi: 10.1016/j.asoc.2007.05.010.
  • [2] X. Zhang and M. M. Khan, “Intelligent Vehicle Navigation and Traffic System,” in Principles of Intelligent Automobiles, Singapore: Springer Singapore, 2019, pp. 175–209. doi: 10.1007/978-981-13-2484-0_5.
  • [3] J. Jin and X. Ma, “A group-based traffic signal control with adaptive learning ability,” Eng Appl Artif Intell, vol. 65, pp. 282–293, Oct. 2017, doi: 10.1016/j.engappai.2017.07.022.
  • [4] J.-Z. Yuan, H. Chen, B. Zhao, and Y. Xu, “Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections,” J Comput Sci Technol, vol. 32, no. 6, pp. 1150–1161, Nov. 2017, doi: 10.1007/s11390-017-1790- 3.
  • [5] C. Ma, W. Hao, A. Wang, and H. Zhao, “Developing a Coordinated Signal Control System for Urban Ring Road Under the Vehicle-Infrastructure Connected Environment,” IEEE Access, vol. 6, pp. 52471–52478, 2018, doi: 10.1109/ACCESS.2018.2869890.
  • [6] S. Zhang, R. Benenson, M. Omran, J. Hosang, and B. Schiele, “Towards Reaching Human Performance in Pedestrian Detection,” IEEE Trans Pattern Anal Mach Intell, vol. 40, no. 4, pp. 973–986, Apr. 2018, doi: 10.1109/TPAMI.2017.2700460.
  • [7] J. Li, X. Liang, S. Shen, T. Xu, J. Feng, and S. Yan, “Scale-aware Fast R-CNN for Pedestrian Detection,” IEEE Trans Multimedia, pp. 1–1, 2017, doi: 10.1109/TMM.2017.2759508.
  • [8] B. Ma, S. Lakshmanan, and A. O. Hero, “Simultaneous detection of lane and pavement boundaries using model-based multisensor fusion,” IEEE Transactions on Intelligent Transportation Systems, vol. 1, no. 3, pp. 135–147, 2000, doi: 10.1109/6979.892150.
  • [9] J. Sparbert, K. Dietmayer, and D. Streller, “Lane detection and street type classification using laser range images,” in ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585), pp. 454–459. doi: 10.1109/ITSC.2001.948700.
  • [10] M. Bertozzi and A. Broggi, “GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection,” IEEE Transactions on Image Processing, vol. 7, no. 1, pp. 62–81, 1998, doi: 10.1109/83.650851.
  • [11] Y. Wang, E. K. Teoh, and D. Shen, “Lane detection and tracking using B-Snake,” Image Vis Comput, vol. 22, no. 4, pp. 269–280, Apr. 2004, doi: 10.1016/j.imavis.2003.10.003.
  • [12] Luo-Wei Tsai, Jun-Wei Hsieh, Chi-Hung Chuang, and Kuo-Chin Fan, “Lane detection using directional random walks,” in 2008 IEEE Intelligent Vehicles Symposium, Jun. 2008, pp. 303–306. doi: 10.1109/IVS.2008.4621271.
  • [13] Q. Li, N. Zheng, and H. Cheng, “Springrobot: A Prototype Autonomous Vehicle and Its Algorithms for Lane Detection,” IEEE Transactions on Intelligent Transportation Systems, vol. 5, no. 4, pp. 300–308, Dec. 2004, doi: 10.1109/TITS.2004.838220.
  • [14] Y. Wang, E. K. Teoh, and D. Shen, “Lane detection and tracking using B-Snake,” Image Vis Comput, vol. 22, no. 4, pp. 269–280, Apr. 2004, doi: 10.1016/j.imavis.2003.10.003.
  • [15] J. M. Alvarez, T. Gevers, Y. LeCun, and A. M. Lopez, “Road Scene Segmentation from a Single Image,” 2012, pp. 376–389. doi: 10.1007/978-3-642-33786-4_28.
  • [16] G. L. Oliveira, W. Burgard, and T. Brox, “Efficient deep models for monocular road segmentation,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Oct. 2016, pp. 4885–4891. doi: 10.1109/IROS.2016.7759717.
  • [17] H. Liu, X. Han, X. Li, Y. Yao, P. Huang, and Z. Tang, “Deep representation learning for road detection using Siamese network,” Multimed Tools Appl, vol. 78, no. 17, pp. 24269–24283, Sep. 2019, doi: 10.1007/s11042-018-6986-1.
  • [18] C.-A. Brust, S. Sickert, M. Simon, E. Rodner, and J. Denzler, “Convolutional Patch Networks with Spatial Prior for Road Detection and Urban Scene Understanding,” Feb. 2015.
  • [19] D. T. Nguyen, W. Li, and P. O. Ogunbona, “Human detection from images and videos: A survey,” Pattern Recognit, vol. 51, pp. 148–175, Mar. 2016, doi: 10.1016/j.patcog.2015.08.027.
  • [20] Y. Kim and T. Moon, “Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks,” IEEE Geoscience and Remote Sensing Letters, vol. 13, no. 1, pp. 8–12, Jan. 2016, doi: 10.1109/LGRS.2015.2491329.
  • [21] W. Ouyang and X. Wang, “Joint deep learning for pedestrian detection,” Proceedings of the IEEE International Conference on Computer Vision, pp. 2056–2063, 2013, doi: 10.1109/ICCV.2013.257.
  • [22] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT Press, 2016.
  • [23] S. Srinivas, R. K. Sarvadevabhatla, K. R. Mopuri, N. Prabhu, S. S. S. Kruthiventi, and R. V. Babu, “A Taxonomy of Deep Convolutional Neural Nets for Computer Vision,” Front Robot AI, Jan. 2016, doi: 10.3389/frobt.2015.00036.
  • [24] V. Nair and G. E. Hinton, “Rectified Linear Units Improve Restricted Boltzmann Machines,” in Proceedings of the 27th International Conference on International Conference on Machine Learning, 2010, pp. 807–814.
  • [25] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” Journal of Machine Learning Research, vol. 15, pp. 1929–1958, 2014, doi: 10.1214/12-AOS1000.
  • [26] R. Padilla, S. L. Netto, and E. A. B. da Silva, “A Survey on Performance Metrics for Object-Detection Algorithms,” in 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Jul. 2020, pp. 237–242. doi: 10.1109/IWSSIP48289.2020.9145130.
  • [27] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2016, pp. 779–788. doi: 10.1109/CVPR.2016.91.
  • [28] Y. Tian, G. Yang, Z. Wang, H. Wang, E. Li, and Z. Liang, “Apple detection during different growth stages in orchards using the improved YOLO-V3 model,” Comput Electron Agric, vol. 157, pp. 417–426, Feb. 2019, doi: 10.1016/j.compag.2019.01.012.
  • [29] C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” Jul. 2022.
  • [30] V. Badrinarayanan, A. Kendall, and R. Cipolla, “SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation,” IEEE Trans Pattern Anal Mach Intell, vol. 39, no. 12, pp. 2481–2495, Dec. 2017, doi: 10.1109/TPAMI.2016.2644615.
  • [31] M. Everingham, L. Van~Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The Pascal Visual Object Classes Challenge 2012 (VOC2012) Results.”
  • [32] B. , Dwyer, J. , Nelson, J. , Solawetz, and et. al., “Roboflow (Version 1.0),” https://roboflow.com., 2022.
  • [33] C.-Y. Wang, A. Bochkovskiy, and H.-Y. M. Liao, “YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors,” Jul. 2022.
  • [34] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” in International Conference on Learning Representations, Sep. 2015.
  • [35] Ö. Dülger, H. Oğuztüzün, and M. Demirekler, “Memory Coalescing Implementation of Metropolis Resampling on Graphics Processing Unit,” J Signal Process Syst, vol. 90, no. 3, pp. 433–447, Mar. 2018, doi: 10.1007/s11265-017- 1254-6.
  • [36] X. Song, T. Rui, S. Zhang, J. Fei, and X. Wang, “The Road Segmentation Method Based on the Deep Auto-Encoder with Supervised Learning,” Computers & Electrical Engineering, vol. 68, pp. 381–388, 2018, doi: 10.1007/978-3-319- 69877-9_28.
  • [37] B. L. Priya, S. Jayalakshmy, G. Idayachandran, and S. Kumaran, “Performance Analysis of Semantic Segmentation using Optimized CNN based SegNet,” in 2022 International Conference on Smart Technologies and Systems for Next Generation Computing (ICSTSN), Mar. 2022, pp. 1–5. doi: 10.1109/ICSTSN53084.2022.9761293.
  • [38] W. Nagai, T. Katayama, T. Song, and T. Shimamoto, “High Efficiency Dataset Generation for Semantic Video Segmentation on Road Intersection,” in 2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), Jul. 2022, pp. 1–4. doi: 10.1109/ITC-CSCC55581.2022.9894901.
  • [39] Yunsheng Jiang and Jinwen Ma, “Combination features and models for human detection,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2015, pp. 240–248. doi: 10.1109/CVPR.2015.7298620.
  • [40] H. Htet Lin, “Person detection based on fusion histogram of gradients with texture (FHGT) local features,” International Journal of Research in Computer Scientific (IJRCS), vol. 5, no. 4, 2018.
  • [41] H. Htet Lin, “Smart Feature Fusion and Model for Human Detection,” Review of Computer Engineering Research, vol. 7, no. 1, pp. 38–46, 2020, doi: 10.18488/journal.76.2020.71.38.46.
  • [42] I. Oztel, “Human Detection System using Different Depths of the Resnet-50 in Faster R-CNN,” in 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Oct. 2020, pp. 1–5. doi: 10.1109/ISMSIT50672.2020.9255109.
APA Yolcu G, Oztel I (2023). Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles. , 22 - 31. 10.35377/saucis...1170902
Chicago Yolcu Gozde,Oztel Ismail Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles. (2023): 22 - 31. 10.35377/saucis...1170902
MLA Yolcu Gozde,Oztel Ismail Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles. , 2023, ss.22 - 31. 10.35377/saucis...1170902
AMA Yolcu G,Oztel I Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles. . 2023; 22 - 31. 10.35377/saucis...1170902
Vancouver Yolcu G,Oztel I Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles. . 2023; 22 - 31. 10.35377/saucis...1170902
IEEE Yolcu G,Oztel I "Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles." , ss.22 - 31, 2023. 10.35377/saucis...1170902
ISNAD Yolcu, Gozde - Oztel, Ismail. "Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles". (2023), 22-31. https://doi.org/10.35377/saucis...1170902
APA Yolcu G, Oztel I (2023). Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles. Sakarya University Journal of Computer and Information Sciences (Online), 6(1), 22 - 31. 10.35377/saucis...1170902
Chicago Yolcu Gozde,Oztel Ismail Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles. Sakarya University Journal of Computer and Information Sciences (Online) 6, no.1 (2023): 22 - 31. 10.35377/saucis...1170902
MLA Yolcu Gozde,Oztel Ismail Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles. Sakarya University Journal of Computer and Information Sciences (Online), vol.6, no.1, 2023, ss.22 - 31. 10.35377/saucis...1170902
AMA Yolcu G,Oztel I Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles. Sakarya University Journal of Computer and Information Sciences (Online). 2023; 6(1): 22 - 31. 10.35377/saucis...1170902
Vancouver Yolcu G,Oztel I Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles. Sakarya University Journal of Computer and Information Sciences (Online). 2023; 6(1): 22 - 31. 10.35377/saucis...1170902
IEEE Yolcu G,Oztel I "Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles." Sakarya University Journal of Computer and Information Sciences (Online), 6, ss.22 - 31, 2023. 10.35377/saucis...1170902
ISNAD Yolcu, Gozde - Oztel, Ismail. "Deep Learning-based Road Segmentation & Pedestrian Detection System for Intelligent Vehicles". Sakarya University Journal of Computer and Information Sciences (Online) 6/1 (2023), 22-31. https://doi.org/10.35377/saucis...1170902
Sayfa Oluşturulma Tarihi
24-04-2024 6:49

İLETİŞİM

TÜBİTAK ULAKBİM TR Dizin

Yüzüncüyıl, İşçi Blokları Mahallesi

Muhsin Yazıcıoğlu Caddesi No:51/C

06530 Çankaya / ANKARA +90 (312) 298 92 00

trdizin@tubitak.gov.tr

TÜBİTAK ULAKBİM Ulusal Akademik Ağ ve Bilgi Merkezi Cahit Arf Bilgi Merkezi © Tüm Hakları Saklıdır. Gizlilik Politikası Kullanım Şartları