Object Detection Distance Estimation

Authors

  • K Usha Rani  Associate Professor and HOD, Department of CSE, Bhoj Reddy Engineering College for Women, Hyderabad, Telangana, India
  • Puram Shravya  Department of CSE, Bhoj Reddy Engineering College for Women, Hyderabad, Telangana, India
  • Kotam Sreeja Reddy  Department of CSE, Bhoj Reddy Engineering College for Women, Hyderabad, Telangana, India

Keywords:

Artificial Intelligence, Object Detection, Camera, Focus Distance, Distance.

Abstract

Artificial intelligence technology is developing rapidly in recent years. The purpose of this paper is to measure the distance to an object using this. In order to measure the distance, two separate pictures from same angles of the object will be taken. It extracts sizes for the same object in two pictures. In order to do this in real time, object detection technology of Artificial Intelligence on mobile phone was used. In this paper, a method for measuring the distance from two pictures is presented. The proposed method was implemented as a prototype on iOS. In order to measure the performance of distance measurement, experiments were conducted in various environments. In the experiments, the empirical data yielded some discrepancies with the actual measurement. This was a result of errors occurring in the object detection process where the actual size of the object was calculated. Despite these discrepancies, this method of object detection may be widely used in instances where accurate measurements are not necessarily required such as guidance systems for the visually impaired.

References

  1. SHIN HC, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE transactions on medical imaging. 2016;35(5): 1285-1298. DOI: 10.1109/TMI.2016.2528162.
  2. Heng S, Minghao X, Ran L. Deep learning for household load forecasting—A novel pooling deep RNN. IEEE Transactions on Smart Grid. 2017;9(5): 5271-5280. DOI: 10.1109/TSG.2017.2686012.
  3. Shaoqing R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems. 2015: 91-99.
  4. Ross G. Fast r-cnn. Proceedings of the IEEE international conference on computer vision. 2015: 1440-1448.
  5. Jeff T. Intelligent Mobile Projects with TensorFlow: Build 10+ Artificial Intelligence Apps Using TensorFlow Mobile and Lite for IOS, Android, and Raspberry Pi. Packt Publishing Ltd, 2018.
  6. Joseph R, et al. You only look once: Unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 779-788.
  7. Rachel H, Jonathan P, Cuixian C. YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. IEEE International Conference on Big Data. 2018: 2503-2510.
  8. Masahiro K, et al. Axi-Vision Camera (real-time distance-mapping camera). Applied Optics. 2000;39(22): 3931-3939. DOI:10.1364/AO.39.003931.
  9. Abir RK, et al. Person to camera distance measurement based on eye-distance. Third International Conference on Multimedia and Ubiquitous Engineering, IEEE. 2009: 137-141. DOI: 10.1109/MUE.2009.34.
  10. Arturo F, et al. Camera distance from face images. International Symposium on Visual Computing. Springer, Berlin, Heidelberg. 2013: 513-522.
  11. Zaarane A, et al. Distance measurement system for autonomous vehicles using stereo camera. Array. 2020 Mar; 5; 100016. DOI:10.1016/j.array.2020.100016.
  12. Robert J W, Xiang L, Charles XL. Pelee. A real-time object detection system on mobile devices. Advances in Neural Information Processing Systems. 2018: 1963-1972.
  13. Yan W, et al. Pseudo-lidar from visual depth estimation: Bridging the gap in 3d object detection for autonomous driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 8445-8453.
  14. Satapathy, S.K., Mishra, S., Mallick, P.K. et al. ADASYN and ABC-optimized RBF convergence network for classification of electroencephalograph signal. Pers Ubiquit Comput (2021). https://doi.org/10.1007/s00779-021-01533-4
  15. Bisoy, S. K., Mallick, P. K., & Mishra, A. Fairness Analysis of TCP Variants in Asymmetric Network. International Journal of Engineering & Technology, 7(2.12), 231-233.

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Published

2023-08-30

Issue

Section

Research Articles

How to Cite

[1]
K Usha Rani, Puram Shravya, Kotam Sreeja Reddy, " Object Detection Distance Estimation" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 4, pp.94-101, July-August-2023.