Research Article
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Recognition of Immobile Objects with an Active Infrared Sensor at Short Distances

Year 2022, Volume 13, Issue 1, 63 - 70, 09.06.2022
https://doi.org/10.29048/makufebed.1009311

Abstract

With the technological developments, autonomous machines have become used in most areas. The sensors used in these studies are of great importance. By means of sensors, objects can be identified, counted, positioned and classified. Infrared sensors are among the types of sensors that have taken their place in these activities. In this study, their numbers, areas, positions and classifications have been carried out in order to recognize the models at close range with the active infrared sensor. A three-dimensional infrared sensor system has been created with a unidimensional active infrared sensor. This sensor system stands out with its efficiency and cost.

References

  • Badamasi, Y.A. (2014). The working principle of an Arduino. The 11th International Conference on Electronics, Computer and Computation (ICECCO), Abuja, Nigeria, 1-4.
  • Besl, P.J. (1989). Active optical range imaging sensors. In: Advances in machine vision, Springer, 1-63.
  • Chen, J., Xu, H., Wu, J., Yue, R., Yuan, C., Wang, L.J.I.A. (2019). Deer crossing road detection with roadside LiDAR sensor. IEEE Access, 7: 65944-65954.
  • Csaba, G., Somlyai, L., Vámossy, Z. (2018). Mobil robot navigation using 2D LIDAR. The 2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI), Kosice and Herlany, Slovakia, 143-148.
  • Filliat, D., Battesti, E., Bazeille, S., Duceux, G., Gepperth, A., Harrath, L., Jebari, I., Pereira, R., Tapus, A., Meyer, C. (2012). RGBD object recognition and visual texture classification for indoor semantic mapping. The 2012 IEEE International Conference on Technologies for Practical Robot Applications (TePRA), Woburn, MA, USA, 127-132.
  • Klavestad, S., Assres, G., Fagernes, S., Grønli, T.-M.J.I. (2020). Monitoring activities of daily living using UWB radar technology: A contactless approach. IOT, 1(2): 320-336.
  • Klein, L.A. (2001). Sensor technologies and data requirements for ITS. 685 Canton Street Norwood, MA United States, 02062: Artech House Publishers.
  • Mambou, S.J., Maresova, P., Krejcar, O., Selamat, A., Kuca, K.J.S. (2018). Breast cancer detection using infrared thermal imaging and a deep learning model. Sensors, 18(9): 2799; DOI 10.3390/s18092799
  • Martin, P., Feng, Y., Wang, X.J.U.o.U.T.L. (2003). Final Report. Detector Technology Evaluation, Department of Civil and Environmental Engineering.
  • Molchanov, P., Gupta, S., Kim, K., Pulli, K. (2015). Short-range FMCW monopulse radar for hand-gesture sensing. The 2015 IEEE Radar Conference (RadarCon), Arlington, VA, USA, 1491–1496.
  • Ruser, H. (2005). Object recognition with a smart low-cost active infrared sensor array. The 1st International Conference on Sensing Tech., 494-499.
  • Suryadevara, N.K., Mukhopadhyay, S.C.J.I.s.j. (2012). Wireless sensor network based home monitoring system for wellness determination of elderly. IEEE Sensors Journal, 12(6): 1965-1972.
  • Tiaprasert, K., Zhang, Y., Wang, X. B., Zeng, X. (2015). Queue length estimation using connected vehicle technology for adaptive signal control. IEEE Transactions on Intelligent Transportation Systems, 16(4): 2129-2140.
  • Wang, C., Zhang, X., Hu, W.J.C.S.R. (2020). Organic photodiodes and phototransistors toward infrared detection: materials, devices, and applications. The Royal Society of Chemistry, 49(3): 653-670.
  • Wehr, A., Lohr, U. (1999). Airborne laser scanning—an introduction and overview. ISPRS Journal of photogrammetry and remote sensing, 54(2-3):, 68-82.
  • Wu, J., Xu, H., Tian, Y., Pi, R., Yue, R.J.S. (2020a). Vehicle detection under adverse weather from roadside LiDAR data. Sensors, 20(12), 3433; DOI: 10.3390/s20123433
  • Wu, J., Xu, H., Zhang, Y., Tian, Y., Song, X. (2020b). Real-time queue length detection with roadside LiDAR data. Sensors, 20(8), 2342; DOI: 10.3390/s20082342
  • Wu, J., Wang, N., Yan, X., Wang, H.J.N.R. (2021). Emerging low-dimensional materials for mid-infrared detection. Nano Research, 14(6): 1863-1877.
  • Zhao, J., Xu, H., Liu, H., Wu, J., Zheng, Y., Wu, D. (2019). Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors. Transportation Research Part C: Emerging Technologies, 100: 68-87.
  • Zhao, Y., Su, Y. (2017). Vehicles detection in complex urban scenes using Gaussian mixture model with FMCW radar. IEEE Sensors Journal, 17(18): 5948-5953.

Kısa Mesafelerde Aktif Kızılötesi Sensörle Hareketsiz Cisimlerin Tanımlanması

Year 2022, Volume 13, Issue 1, 63 - 70, 09.06.2022
https://doi.org/10.29048/makufebed.1009311

Abstract

Teknolojik gelişmeler ile birlikte çoğu alanda otonomlaşan makineler kullanılır hale gelmiştir. Bu çalışmalarda kullanılan sensörler, çok büyük önem taşımaktadır. Sensörler vasıtasıyla cisimlerin tanımlanması, sayılması, konumlarının tespit edilmesi ve sınıflandırılması yapılabilmektedir. Kızılötesi sensörler bu faaliyetler içerisinde kendine yer edinmiş sensör çeşitlerindendir. Bu çalışmada, kapalı bir oda ortamında aktif kızılötesi sensör ile yakın mesafedeki modellerin tanımlanabilmeleri için sayıları, boyutları, konumları ve sınıflandırmaları gerçekleştirilmiştir. Tek-boyutlu çalışan aktif kızılötesi sensör ile üç-boyutlu çalışabilen bir aktif kızılötesi sensör sistemi oluşturulmuştur. Bu sensör sistemi verimliliği ve maliyeti ile öne çıkmaktadır.

References

  • Badamasi, Y.A. (2014). The working principle of an Arduino. The 11th International Conference on Electronics, Computer and Computation (ICECCO), Abuja, Nigeria, 1-4.
  • Besl, P.J. (1989). Active optical range imaging sensors. In: Advances in machine vision, Springer, 1-63.
  • Chen, J., Xu, H., Wu, J., Yue, R., Yuan, C., Wang, L.J.I.A. (2019). Deer crossing road detection with roadside LiDAR sensor. IEEE Access, 7: 65944-65954.
  • Csaba, G., Somlyai, L., Vámossy, Z. (2018). Mobil robot navigation using 2D LIDAR. The 2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI), Kosice and Herlany, Slovakia, 143-148.
  • Filliat, D., Battesti, E., Bazeille, S., Duceux, G., Gepperth, A., Harrath, L., Jebari, I., Pereira, R., Tapus, A., Meyer, C. (2012). RGBD object recognition and visual texture classification for indoor semantic mapping. The 2012 IEEE International Conference on Technologies for Practical Robot Applications (TePRA), Woburn, MA, USA, 127-132.
  • Klavestad, S., Assres, G., Fagernes, S., Grønli, T.-M.J.I. (2020). Monitoring activities of daily living using UWB radar technology: A contactless approach. IOT, 1(2): 320-336.
  • Klein, L.A. (2001). Sensor technologies and data requirements for ITS. 685 Canton Street Norwood, MA United States, 02062: Artech House Publishers.
  • Mambou, S.J., Maresova, P., Krejcar, O., Selamat, A., Kuca, K.J.S. (2018). Breast cancer detection using infrared thermal imaging and a deep learning model. Sensors, 18(9): 2799; DOI 10.3390/s18092799
  • Martin, P., Feng, Y., Wang, X.J.U.o.U.T.L. (2003). Final Report. Detector Technology Evaluation, Department of Civil and Environmental Engineering.
  • Molchanov, P., Gupta, S., Kim, K., Pulli, K. (2015). Short-range FMCW monopulse radar for hand-gesture sensing. The 2015 IEEE Radar Conference (RadarCon), Arlington, VA, USA, 1491–1496.
  • Ruser, H. (2005). Object recognition with a smart low-cost active infrared sensor array. The 1st International Conference on Sensing Tech., 494-499.
  • Suryadevara, N.K., Mukhopadhyay, S.C.J.I.s.j. (2012). Wireless sensor network based home monitoring system for wellness determination of elderly. IEEE Sensors Journal, 12(6): 1965-1972.
  • Tiaprasert, K., Zhang, Y., Wang, X. B., Zeng, X. (2015). Queue length estimation using connected vehicle technology for adaptive signal control. IEEE Transactions on Intelligent Transportation Systems, 16(4): 2129-2140.
  • Wang, C., Zhang, X., Hu, W.J.C.S.R. (2020). Organic photodiodes and phototransistors toward infrared detection: materials, devices, and applications. The Royal Society of Chemistry, 49(3): 653-670.
  • Wehr, A., Lohr, U. (1999). Airborne laser scanning—an introduction and overview. ISPRS Journal of photogrammetry and remote sensing, 54(2-3):, 68-82.
  • Wu, J., Xu, H., Tian, Y., Pi, R., Yue, R.J.S. (2020a). Vehicle detection under adverse weather from roadside LiDAR data. Sensors, 20(12), 3433; DOI: 10.3390/s20123433
  • Wu, J., Xu, H., Zhang, Y., Tian, Y., Song, X. (2020b). Real-time queue length detection with roadside LiDAR data. Sensors, 20(8), 2342; DOI: 10.3390/s20082342
  • Wu, J., Wang, N., Yan, X., Wang, H.J.N.R. (2021). Emerging low-dimensional materials for mid-infrared detection. Nano Research, 14(6): 1863-1877.
  • Zhao, J., Xu, H., Liu, H., Wu, J., Zheng, Y., Wu, D. (2019). Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors. Transportation Research Part C: Emerging Technologies, 100: 68-87.
  • Zhao, Y., Su, Y. (2017). Vehicles detection in complex urban scenes using Gaussian mixture model with FMCW radar. IEEE Sensors Journal, 17(18): 5948-5953.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Research Paper
Authors

Abdülkadir ÇILDIR> (Primary Author)
Süleyman Demirel Üniversitesi
0000-0003-1789-6088
Türkiye


Mesud KAHRİMAN>
SÜLEYMAN DEMİREL ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ
0000-0003-0731-0936
Türkiye


Mesut TİGDEMİR>
SÜLEYMAN DEMİREL ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ
0000-0002-5303-2722
Türkiye

Publication Date June 9, 2022
Published in Issue Year 2022, Volume 13, Issue 1

Cite

APA Çıldır, A. , Kahriman, M. & Tigdemir, M. (2022). Kısa Mesafelerde Aktif Kızılötesi Sensörle Hareketsiz Cisimlerin Tanımlanması . Mehmet Akif Ersoy Üniversitesi Fen Bilimleri Enstitüsü Dergisi , 13 (1) , 63-70 . DOI: 10.29048/makufebed.1009311

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