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Thermal image processing-based intelligent technique for object detection

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Abstract

The Thermal Image Processing Technique (TIPT) is the most prominent tool for observing an object at night. It is vastly used in many domains like security, healthcare, process control, and surveillance especially in defense vehicles where visualization at night would also mandatory for checking. In this paper, a thermal imaging camera is proposed to only be felicitated in regular as well as automated vehicles for better identification of objects especially at night when visibility is very less. Due to the huge variation in grayscales and pseudo-coloring values in the thermal image, a fuzzy-based CNN [FCNN] model is proposed to be applied to identify the boundaries of the objects. In this technique, the correlation between the thermal images of the moving object and its types is proposed to be trained with the novel FCNN model. The framed methodology is not only implementable in benched marked video datasets but also applicable in real-life conditions on the ground experimental scenario on a live feed video streaming. The results significantly indicate the enhancement of visual capacity in the TIPT compared to normal visual technique.

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References

  1. Winter, J., Stein, M.A.: Computer image processing techniques for automated breast thermogram interpretation. Comput. Biomed. Res. 6(6), 522–529 (1973)

    Article  Google Scholar 

  2. Kano, Y., and Wolf E. (1962) Temporal coherence of black body radiation. Proc. Phys. Soci. (1958–1967) 80(6): 1273

  3. Nam, Y., Nam, Y.-C.: Vehicle classification based on images from visible light and thermal cameras. EURASIP J. Image Video Process. 2018(1), 1–9 (2018)

    Article  Google Scholar 

  4. Vojco, K., Aleksandar A., Dragan S., Mile S., and Tatjana K. Intelligent control of object detection: image processing and pathfinding problem. In Proceedings World Automation Congress, 2004., 17: 131–136. IEEE, 2004.

  5. Browne, M., and Ghidary S. S. Convolutional neural networks for image processing: an application in robot vision. In Australasian Joint Conference on Artificial Intelligence, pp. 641–652. Springer, Berlin, Heidelberg, 2003.

  6. Shi, R., Ngan K. N., Li S. Jaccard index compensation for object segmentation evaluation. In 2014 IEEE International Conference on Image Processing (ICIP), pp. 4457–4461. IEEE, 2014.

  7. Thakur, R. Infrared Sensors for object detection. In Recent Development in Optoelectronic Devices. IntechOpen, 2017.

  8. Alam, M.S., Bognar, J.G., Hardie, R.C., Yasuda, B.J.: Infrared image registration and high-resolution reconstruction using multiple translationally shifted aliased video frames. IEEE Trans. Instrum. Meas. 49(5), 915–923 (2000)

    Article  Google Scholar 

  9. Giacomin, J. Thermal: seeing the world through 21st century eyes. Papadakis, 2010.

  10. Rossignoli, I., Benito, P.J., Herrero, A.J.: Reliability of infrared thermography in skin temperature evaluation of wheelchair users. Spinal Cord 53(3), 243–248 (2015)

    Article  Google Scholar 

  11. Burigana, L., Magnini, L.: Image processing and analysis of radar and lidar data: new discoveries in Verona southern lowland (Italy). STAR: Sci. Technol. Archaeol. Res. 3(2), 490–509 (2017)

    Article  Google Scholar 

  12. Rakrueangdet, K., Nunak N., Suesut T., Sritham E. Emissivity measurements of reflective materials using infrared thermography. In International MultiConference of Engineers and Computer Scientists (IMECS). 2016.

  13. Caillas, C. Thermal imaging for object detection in outdoor scenes. In EEE International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications, pp. 651–658. IEEE, 1990.

  14. Tsukamoto, T., Tanaka, S.: Patternable temperature sensitive paint using Eu (TTA) 3 for the micro thermal imaging. J. Phys.: Conf. Ser. 476(1), 012073 (2013)

    Google Scholar 

  15. de Bittencourt MMGT, and Gonzaga A. "Digital image processing techniques for uncooled LWIR thermal camera." In Electro-Optical and Infrared Systems: Technology and Applications IX, vol. 8541, p. 85410Z. International Society for Optics and Photonics, 2012.

  16. Liu, G., Liu, Z., Liu, S., Ma, J., Wang, F.: Registration of infrared and visible light image based on visual saliency and scale invariant feature transform. EURASIP J. Image Video Process. 2018(1), 1–12 (2018)

    Article  Google Scholar 

  17. LeBeau, T. Thermal imaging for safer object detection. In Infrared Technology and Applications XLV, vol. 11002, p. 110021H. International Society for Optics and Photonics, 2019.

  18. Tan, S.-T., Chen, K., Ong, S., Chew, W.: Utilization of spectral vector properties in multivariate chemometrics analysis of hyperspectral infrared imaging data for cellular studies. Analyst 133(10), 1395–1408 (2008)

    Article  Google Scholar 

  19. Nath, S., Agarwal, S., Pandey, G.N.: Mathematical Foundation Based Inter-Connectivity modelling of Thermal Image processing technique for Fire Protection. EAI Endorsed Trans. Creat. Technol. 2(5), 150097 (2015)

    Article  Google Scholar 

  20. Peterson, B.J.: Infrared imaging video bolometer. Rev. Sci. Instrum. 71(10), 3696–3701 (2000)

    Article  Google Scholar 

  21. Lawrence, M., Ashley S. F., Lupton M., McEwen R. K., Wilson M. (2007) Signal processing core for high performance thermal imaging. In Infrared Technology and Applications XXXIII, vol. 6542, p. 654215. International Society for Optics and Photonics.

  22. Zhang, J., Jia, X., Li, J.: Integration of scanning and image processing algorithms for lane detection based on fuzzy method. J. Intell. Fuzzy Syst. 29(6), 2779–2786 (2015)

    Article  Google Scholar 

  23. Pal, S. K. "Fuzzy sets in image processing and recognition." In [1992 Proceedings] IEEE International Conference on Fuzzy Systems, pp. 119–126. IEEE, 1992.

  24. Iqbal, Q. M., Choudhury J. P., De M. "Image recognition and processing using Artificial Neural Network." In 2012 1st International Conference on Recent Advances in Information Technology (RAIT), 2012.

  25. Li T., Wang Y., Chen Z., and Wang R. "Linear feature extraction for infrared image." In Image Extraction, Segmentation, and Recognition, vol. 4550, pp. 281–286. International Society for Optics and Photonics, 2001.

  26. Gundogdu, E., Koc A., and Alatan A. A. "Object classification in infrared images using deep representations." In 2016 IEEE International Conference on Image Processing (ICIP), pp. 1066–1070. IEEE, 2016.

  27. Smith, M.S., Silver, E.A., Stein, M.K.: Improving instruction in rational numbers and proportionality, vol. 1. Teachers College Press, New York (2005)

    Google Scholar 

  28. Zheng, D., Lin X., and Wang X. "Image Segmentation Method Based on Spiking Neural Network with Adaptive Synaptic Weights." In 2019 IEEE 4th International Conference on Signal and Image Processing (ICSIP), pp. 1043–1049. IEEE, 2019.

  29. Sharma, S., Sharma, S., Athaiya, A.: Activation functions in neural networks. Towards Data Sci. 6(12), 310–316 (2017)

    Google Scholar 

  30. Galagan, R., and Momot A. "The use of backpropagation artificial neural networks in thermal tomography." In 2018 IEEE First International Conference on System Analysis and Intelligent Computing (SAIC), pp. 1–6. IEEE, 2018.

  31. Jiyao, L.F., Hu, M., Chen, W., Zhan, J.: A novel fuzzy-based convolutional neural network method to traffic flow prediction with uncertain traffic accident information. IEEE Access 7, 20708–20722 (2019)

    Article  Google Scholar 

  32. Kwan, C., Chou, B., Yang, J., Rangamani, A., Tran, T., Zhang, J., Etienne-Cummings, R.: Target tracking and classification using compressive measurements of MWIR and LWIR coded aperture cameras. J. Signal Inf. Process. 10(3), 73–95 (2019)

    Google Scholar 

  33. Kwan, C., Chou, B., Yang, J., Tran, T.: Deep learning based target tracking and classification for infrared videos using compressive measurements. J. Signal Inf. Process. 10(04), 167 (2019)

    Google Scholar 

  34. Kwan, C., Budavari, B.: Enhancing small moving target detection performance in low-quality and long-range infrared videos using optical flow techniques. Remote Sens. 12(24), 4024 (2020)

    Article  Google Scholar 

  35. Kwan, C., Chou B., Yang J., Tran T. "Compressive object tracking and classification using deep learning for infrared videos." In Pattern Recognition and Tracking XXX, vol. 10995, p. 1099506. International Society for Optics and Photonics.

  36. Kwan, C., Gribben D., and Tran T. "Multiple human objects tracking and classification directly in compressive measurement domain for long range infrared videos." In 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), pp. 0469–0475. IEEE, 2019.

  37. Sibi, P., Jones, S.A., Siddarth, P.: Analysis of different activation functions using back propagation neural networks. J. Theor. Appl. Inf. Technol. 47(3), 1264–1268 (2013)

    Google Scholar 

  38. Agostinelli F., Hoffman M., Sadowski P., Baldi P. Learning activation functions to improve deep neural networks. arXiv:1412.6830.

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Authors and Affiliations

  1. Post-Doctoral Fellow, National Institute of Technology, Tiruchirappalli, India

    Sayantan Nath & C. Mala

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  1. Sayantan Nath
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  2. C. Mala
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Correspondence to C. Mala.

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Nath, S., Mala, C. Thermal image processing-based intelligent technique for object detection. SIViP 16, 1631–1639 (2022). https://doi.org/10.1007/s11760-021-02118-7

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