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Derin Öğrenme Teknikleri İle Nesne Tespiti Ve Takibi Üzerine Bir İnceleme

Yıl 2021, Cilt , Sayı 25, 159 - 171, 08.06.2021
https://doi.org/10.31590/ejosat.878552

Öz

Derin öğrenme, son zamanlarda insan hatalarını en aza indirmesiyle popüler olan yapay zekâ yaklaşımlarındandır. Derin öğrenme teknikleri birçok alanda büyük miktardaki veri kullanımı ile başarılı bir şekilde algılama, yorumlama yapabilme yeteneğine sahiptir. Özellikle görüntü işleme alanında birikmiş etiketli verilerdeki hızlı artış derin öğrenme algoritmalarına yönelmeyi zorunlu hale getirmiştir. Bu alanlardaki verilerin giderek artmasıyla büyük verilerden yararlı bilgiyi ayırmak ve metin, görüntü, ses dosyalarına anlam kazandırmak amacıyla derin öğrenme yöntemleri kullanılmaktadır. Son yıllarda, nesne tespiti ve nesne takibi alanında yapılan çalışmalarda artış görülmektedir. Videolar gibi durağan olmayan görüntüler üzerinde tespit ve analiz sonrasında takip edilecek olan bir nesne varsa anlamlı bilgiler çıkarmak daha zor olmaktadır. Bu gibi durumlarda derin öğrenme algoritmalarının kullanılması görüntü işleme problemlerinin kolaylıkla çözüme kavuşturulabilmesini sağlamaktadır. Bu çalışmanın amacı; derin öğrenme ile nesne tespiti ve takibi konusunda yapılan uygulamaları incelemek, son gelişmeleri anlatmak, popüler kütüphaneler, veri setleri, algoritmalar hakkında bilgi vererek bu alanda çalışacak olan araştırmacılara yardımcı olmaktır.

Kaynakça

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A Review On Object Detection And Tracking With Deep Learning Techniques

Yıl 2021, Cilt , Sayı 25, 159 - 171, 08.06.2021
https://doi.org/10.31590/ejosat.878552

Öz

Deep learning is one of the artificial intelligence approaches that has recently become popular for minimizing human error. Deep learning techniques have the ability to successfully detect and interpret with the use of large amounts of data in many areas. Especially, the rapid increase in labeled data accumulated in the field of image processing has made it necessary to turn to deep learning algorithms. With the increasing data in these areas, deep learning methods are used to separate useful information from big data and to give meaning to text, images and audio files. In recent years, there has been an increase in the studies conducted in the field of object detection and object tracking. If there is an object to be followed after detection and analysis on non-stationary images such as videos, it is more difficult to extract meaningful information. In such cases, the use of deep learning algorithms enables image processing problems to be solved easily. The aim of this study is to examine the applications of deep learning and object detection and tracking, to explain the latest developments, to help researchers who will work in this field by giving information about popular libraries, data sets, algorithms.

Kaynakça

  • Amidi, A., Amidi, S. (2020). Derin Öğrenme El Kitabı. Derin Öğrenme El Kitabı: https://stanford.edu/~shervine/l/tr/teaching/cs-229/cheatsheet-deep-learning adresinden alındı.
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  • Avila, S., Thome, N., Cord, M., Valle, E., De A. Araújo, A. (2013). Pooling in image representation: The visual codeword point of view. Computer Vision and Image Understanding, 453-465, doi: 10.1016/j.cviu.2012.09.007.
  • Bewley, A., Ge, Z., Ott, L., Ramos, F., Upcroft, B. (2016). Simple online and realtime tracking. Proceedings - International Conference on Image Processing, ICIP, 3464–3468, doi: 10.1109/ICIP.2017.8296962.
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  • Brunetti, A., Buongiorno, D., Trotta, G., Bevilacqua, V. (2018). Computer vision and deep learning techniques for pedestrian detection and tracking: A survey. Neurocomputing, doi: 10.1016/j.neucom.2018.01.092.
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  • Chaudhary, S., Khan, M., Bhatnagar, C. (2018). Multiple Anomalous Activity Detection in Videos. Procedia Computer Science, 336-345, doi: 10.1016/j.procs.2017.12.045.
  • Cheng, X., Song, C., Gu, Y., Chen, B. (2020). Learning Attention for Object Tracking with Adversarial Learning Network, doi: 10.21203/rs.3.rs-15512/v3.
  • Ciaparrone, G., Luque Sánchez, F., Tabik, S., Troiano, L., Tagliaferri, R., Herrera, F. (2020). Deep learning in video multi-object tracking: A survey. Neurocomputing, 61-88, doi: 10.1016/j.neucom.2019.11.023.
  • Collobert, R., Farabet, C., Kavukcuoğlu, K. (2017). Torch | Scientific computing for LuaJIT. NIPS Workshop on Machine Learning Open Source Software.
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  • Cömert, O., Hekim, M., Adem, K. (2019). Faster R-CNN Kullanarak Elmalarda Çürük Tespiti. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 335-341, doi: 10.29137/umagd.469929.
  • Daş, R., Polat, B., Tuna, G. (2019). Derin Öğrenme ile Resim ve Videolarda Nesnelerin Tanınması ve Takibi. Fırat Üniversitesi Müh. Bil. Dergisi, 571-581, doi: 10.35234/fumbd.608778.
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  • Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Li, F. (2009). ImageNet: a Large-Scale Hierarchical Image Database. 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25, doi: 10.1109/cvpr.2009.5206848.
  • Deng, L., Yu, D. (2013). Deep learning: Methods and applications. Foundations and Trends in Signal Processing, 197-387, doi: 10.1561/2000000039.
  • Deori, B., Meitei, D. (2014). A survey on moving object tracking in video. International Journal on Information Theory, 31-46, doi: 10.5121/ijit.2014.3304.
  • Everingham, M., Van Gool, L., Williams, C., Winn, J., Zisserman, A. (2008). The PASCAL Visual Object Classes Challenge 2008 (VOC) Results. http://www.pascal-network.org/ challenges/VOC/voc2008/workshop/index.html adresinden alındı.
  • Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 193-202, doi: 10.1007/BF00344251.
  • Geiger, A., Lenz, P., Urtasun, R. (2012). Are we ready for autonomous driving? the KITTI vision benchmark suite. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 3354-3361, doi: 10.1109/CVPR.2012.6248074.
  • Gibney, E. (2016). Google AI algorithm masters ancient game of Go. Nature, doi: 10.1038/529445a.
  • Girshick, R. (2015). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 1440-1448, doi: 10.1109/ICCV.2015.169.
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Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Fatma Gülşah TAN (Sorumlu Yazar)
SÜLEYMAN DEMİREL ÜNİVERSİTESİ
0000-0002-2748-0396
Türkiye


Asım Sinan YÜKSEL
SÜLEYMAN DEMİREL ÜNİVERSİTESİ
0000-0003-1986-5269
Türkiye


Erdal AYDEMİR
SÜLEYMAN DEMİREL ÜNİVERSİTESİ
0000-0003-4834-725X
Türkiye


Mevlüt ERSOY
SÜLEYMAN DEMİREL ÜNİVERSİTESİ
0000-0003-2963-7729
Türkiye

Yayımlanma Tarihi 8 Haziran 2021
Yayınlandığı Sayı Yıl 2021, Cilt , Sayı 25

Kaynak Göster

Bibtex @derleme { ejosat878552, journal = {Avrupa Bilim ve Teknoloji Dergisi}, issn = {}, eissn = {2148-2683}, address = {}, publisher = {Osman SAĞDIÇ}, year = {2021}, volume = {}, pages = {159 - 171}, doi = {10.31590/ejosat.878552}, title = {Derin Öğrenme Teknikleri İle Nesne Tespiti Ve Takibi Üzerine Bir İnceleme}, key = {cite}, author = {Tan, Fatma Gülşah and Yüksel, Asım Sinan and Aydemir, Erdal and Ersoy, Mevlüt} }
APA Tan, F. G. , Yüksel, A. S. , Aydemir, E. & Ersoy, M. (2021). Derin Öğrenme Teknikleri İle Nesne Tespiti Ve Takibi Üzerine Bir İnceleme . Avrupa Bilim ve Teknoloji Dergisi , (25) , 159-171 . DOI: 10.31590/ejosat.878552
MLA Tan, F. G. , Yüksel, A. S. , Aydemir, E. , Ersoy, M. "Derin Öğrenme Teknikleri İle Nesne Tespiti Ve Takibi Üzerine Bir İnceleme" . Avrupa Bilim ve Teknoloji Dergisi (2021 ): 159-171 <https://dergipark.org.tr/tr/pub/ejosat/issue/62595/878552>
Chicago Tan, F. G. , Yüksel, A. S. , Aydemir, E. , Ersoy, M. "Derin Öğrenme Teknikleri İle Nesne Tespiti Ve Takibi Üzerine Bir İnceleme". Avrupa Bilim ve Teknoloji Dergisi (2021 ): 159-171
RIS TY - JOUR T1 - Derin Öğrenme Teknikleri İle Nesne Tespiti Ve Takibi Üzerine Bir İnceleme AU - Fatma Gülşah Tan , Asım Sinan Yüksel , Erdal Aydemir , Mevlüt Ersoy Y1 - 2021 PY - 2021 N1 - doi: 10.31590/ejosat.878552 DO - 10.31590/ejosat.878552 T2 - Avrupa Bilim ve Teknoloji Dergisi JF - Journal JO - JOR SP - 159 EP - 171 VL - IS - 25 SN - -2148-2683 M3 - doi: 10.31590/ejosat.878552 UR - https://doi.org/10.31590/ejosat.878552 Y2 - 2021 ER -
EndNote %0 Avrupa Bilim ve Teknoloji Dergisi Derin Öğrenme Teknikleri İle Nesne Tespiti Ve Takibi Üzerine Bir İnceleme %A Fatma Gülşah Tan , Asım Sinan Yüksel , Erdal Aydemir , Mevlüt Ersoy %T Derin Öğrenme Teknikleri İle Nesne Tespiti Ve Takibi Üzerine Bir İnceleme %D 2021 %J Avrupa Bilim ve Teknoloji Dergisi %P -2148-2683 %V %N 25 %R doi: 10.31590/ejosat.878552 %U 10.31590/ejosat.878552
ISNAD Tan, Fatma Gülşah , Yüksel, Asım Sinan , Aydemir, Erdal , Ersoy, Mevlüt . "Derin Öğrenme Teknikleri İle Nesne Tespiti Ve Takibi Üzerine Bir İnceleme". Avrupa Bilim ve Teknoloji Dergisi / 25 (Haziran 2021): 159-171 . https://doi.org/10.31590/ejosat.878552
AMA Tan F. G. , Yüksel A. S. , Aydemir E. , Ersoy M. Derin Öğrenme Teknikleri İle Nesne Tespiti Ve Takibi Üzerine Bir İnceleme. EJOSAT. 2021; (25): 159-171.
Vancouver Tan F. G. , Yüksel A. S. , Aydemir E. , Ersoy M. Derin Öğrenme Teknikleri İle Nesne Tespiti Ve Takibi Üzerine Bir İnceleme. Avrupa Bilim ve Teknoloji Dergisi. 2021; (25): 159-171.
IEEE F. G. Tan , A. S. Yüksel , E. Aydemir ve M. Ersoy , "Derin Öğrenme Teknikleri İle Nesne Tespiti Ve Takibi Üzerine Bir İnceleme", Avrupa Bilim ve Teknoloji Dergisi, sayı. 25, ss. 159-171, Haz. 2021, doi:10.31590/ejosat.878552