Kısa Mesafelerde Aktif Kızılötesi Sensörle Hareketsiz Cisimlerin Tanımlanması

Yıl 2022, Cilt: 13 Sayı: 1, 63 - 70, 09.06.2022

Abdülkadir Çıldır , Mesud Kahriman , Mesut Tigdemir

https://doi.org/10.29048/makufebed.1009311

Öz

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.

Anahtar Kelimeler

Tek-boyutlu aktif kızılötesi sensör, cisim tanımlama, yakın mesafe, üç-boyut

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

Öz

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.

Anahtar Kelimeler

Unidimensional active infrared sensor, object recognition, close ranging, three-dimension

Kaynakça

• 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.