Heavy Vehicle Detection Using Fine-Tuned Deep Learning

  • Manisha ChateEmail author
  • Vinaya Gohokar
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


Heavy vehicles develop technical snag and traffic jam on streets. Accidents between heavy vehicle and road users, for example, pedestrians often result in severe injuries of the weaker street users. The highway safety and traffic jams can be secured with detection of heavy and overloaded vehicles on the highway to facilitate light motor vehicles like cars, scooters. A model for heavy vehicle detection using fine-tuned based on deep learning is proposed to deal with entangled transportation scene. This model comprises two parts, vehicle detection model and vehicle fine-grained detection. This step provides data for the next classification model. Experiments show that vehicle’s make and model can be recognized from transportation images effectively by using our method. Experimental results demonstrate that the proposed detection system performs accurately with other simple and complex scenarios in detecting heavy vehicles in comparison with past vehicle detection systems.


Heavy vehicles Deep learning Faster R-CNN Transfer learning Driver assistance system 


  1. 1.
    Ge Q, Wen C, Duan S (2014) Fire localization based on range-range-range model for limited interior space. IEEE Trans Instrum Meas 63(9):2223–2237CrossRefGoogle Scholar
  2. 2.
    Sivaraman S, Trivedi MM (2012) Real-time vehicle detection using parts at intersections. In: 15th International IEEE conference on intelligent transportation systems (ITSC). IEEE, pp 1519–1524Google Scholar
  3. 3.
    Chen Z, Ellis T, Velastin S (2012) Vehicle detection, tracking and classification in urban traffic. In: 15th International IEEE conference on intelligent transportation systems (ITSC). IEEE, pp 951–956Google Scholar
  4. 4.
    Kafai M, Bhanu B (2012) Dynamic bayesian networks for vehicle classification in video. IEEE Trans Ind Inf 8(1):100–109CrossRefGoogle Scholar
  5. 5.
    Chen LC, Hsieh JW, Yan Y, Chen DY (2015) Vehicle make and model recognition using sparse representation and symmetrical SURFs. Pattern Recogn 48(6):1979–1998CrossRefGoogle Scholar
  6. 6.
    Li B, Wu T, Zhu SC (2014) Integrating context and occlusion for car detection by hierarchical and-or model. In: Computer vision ECCV 2014. Springer International Publishing, pp 652–667Google Scholar
  7. 7.
    Lin YL, Morariu VI, Hsu W, Davis LS (2014) Jointly optimizing 3d model fitting and fine-grained classification. In: Computer vision–ECCV 2014. Springer International Publishing, pp 466–480Google Scholar
  8. 8.
    Xie S, Yang T, Wang X, Lin Y (2015) Hyper-class augmented and regularized deep learning for fine-grained image classification. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2645–2654Google Scholar
  9. 9.
    Huttunen H, Yancheshmeh FS, Chen K (2016) Car type recognition with deep neural networks. arXiv preprint arXiv:1602.07125
  10. 10.
    Wang Y, Choi J, Morariu VI, Davis LS (2016) Mining discriminative triplets of patches for fine-grained classification. arXiv preprint arXiv:1605.01130
  11. 11.
    Sermanet P, LeCun Y (2011) Traffic sign recognition with multi-scale convolutional networks. In: The 2011 International joint conference on neural networks (IJCNN), July 2011, pp 2809–2813Google Scholar
  12. 12.
    Zang D, Zhang J, Zhang D, Bao M, Cheng J, Tang K (2016) Traffic sign detection based on cascaded convolutional neural networks. In: 2016 17th IEEE/ACIS International Conference on software engineering, artificial intelligence, networking and parallel/distributed computing (SNPD), Shanghai, pp 201–206Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Amity UniversityNoidaIndia
  2. 2.MITPuneIndia

Personalised recommendations