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Video Verilerinde Bulunan Tehlikeli Nesnelerin Derin Öğrenme Yöntemleri ile Tespiti Üzerine Derleme

Yıl 2023, Cilt: 16 Sayı: 1, 60 - 80, 29.06.2023

Ayşe Berika Varol Malkoçoğlu Rüya Şamlı

https://doi.org/10.54525/tbbmd.1184322

Öz

Bilgisayarla görme tekniklerinden biri olan nesne saptaması son yıllarda hem akademik hem de ticarî potansiyeli sayesinde büyük ilgi görmektedir. Günümüzde teknolojinin gelişimi ile birlikte güvenlik ya da kişisel amaçlarla çekilen video görüntülerinin artması ve donanım elemanlarının gelişmesi, ihtiyaç duyulan kaynaklara erişimi kolaylaştırmış dolayısıyla nesne saptama sistemlerinin gelişimini hızlandırmıştır. Bu alanda yaya saptaması, yüz tanıma gibi bazı klasikleşmiş konularda çok sayıda çalışma bulunmaktadır. Fakat bu çalışmada farklı nesne gruplarının getirdiği zorlukları gözlemlemek adına tehlikeli nesneler üzerine yapılan ve güvenlik güçlerine yardımcı sistemlerin tasarlanmasına katkı sağlayan çalışmalar araştırılıp derlenmiştir. Çalışmalarda kullanılan nesne saptama yöntemleri geleneksel yöntemler ve derin öğrenme tabanlı modern yöntemler olarak iki kısımda incelenmiş olup avantajları ve dezavantajları tartışılmıştır. Ayrıca literatürdeki eksiklikler belirlenip, gelecekteki çalışmalar için araştırmacılara yönergeler sunulmuştur.

Anahtar Kelimeler

Nesne tespiti nesne algılama tehlikeli nesneler derin öğrenme

Kaynakça

  • Umut vakfı, http://www.umut.org.tr/ (22.08.2022)
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Review on Detection of Dangerous Objects in Video Data using Deep Learning Methods

Yıl 2023, Cilt: 16 Sayı: 1, 60 - 80, 29.06.2023

Ayşe Berika Varol Malkoçoğlu Rüya Şamlı

https://doi.org/10.54525/tbbmd.1184322

Öz

Object detection, which is one of the computer vision techniques, has been very interested in both academic and commercial potential in recent years. Today, the development of technology, combined with the increased video images for security or personal purposes, and the development of hardware elements, made it easier to access the resources needed, thereby accelerating the development of object detection systems. There are many studies in some classics such as pedestrian detection, face recognition etc. in this area. However, this studies on dangerous objects and contributing to the design of safety-aid systems have been researched and compiled to observe the challenges of different groups of objects in the study. The methods of object detection used in the studies have been studied in two parts as traditional methods and deep learning modern methods, discussing the advantages and disadvantages. In addition, deficiencies in the literature have been identified and guidelines have been provided to researchers for future studies.

Anahtar Kelimeler

Object detection object recognition dangerous objects deep learning

Kaynakça

  • Umut vakfı, http://www.umut.org.tr/ (22.08.2022)
  • UNIDIR, https://unidir.org/ (22.08.2022)
  • United Nation, https://www.un.org/disarmament/ (22.08.2022)
  • Piza, E. L., Welsh, B. C., Farrington, D. P., & Thomas, A. L., “CCTV surveillance for crime prevention: A 40‐year systematic review with meta‐analysis”, Criminology & Public Policy, 18(1):135-159, (2019)
  • Cohen, N., Gattuso, J. & MacLennan-Brown, K., “CCTV operational requirements manual 2009”, St. Albans: Home Office Scientific Development Branch, (2009)
  • Tickner, A. H., & Poulton, E. C., “Monitoring up to 16 synthetic television pictures showing a great deal of movement”, Ergonomics, 16(4):381-401, (1973)
  • Darker, I., Gale, A., Ward, L., & Blechko, A., “Can CCTV reliably detect gun crime?”, In 2007 41st Annual IEEE International Carnahan Conference on Security Technology, 264-271, (2007)
  • Zou, Z., Shi, Z., Guo, Y., & Ye, J., “Object detection in 20 years: A survey”, arXiv preprint arXiv:1905.05055, (2019)
  • Zhao, Z. Q., Zheng, P., Xu, S. T., & Wu, X., “Object detection with deep learning: A review”, IEEE transactions on neural networks and learning systems, 30(11):3212-3232, (2019)
  • Xiao, Y., Tian, Z., Yu, J., Zhang, Y., Liu, S., Du, S., & Lan, X., “A review of object detection based on deep learning”, Multimedia Tools and Applications, 79(33):23729-23791, (2020)
  • [Brunetti, A., Buongiorno, D., Trotta, G. F., & Bevilacqua, V., “Computer vision and deep learning techniques for pedestrian detection and tracking: A survey”, Neurocomputing, 300:17-33, (2018)
  • Wu, X., Kumar, V., Ross Quinlan, J. et al., “Top 10 algorithms in data mining”, Knowledge and information systems, 14:1–37, (2008)
  • Viola, P., & Jones, M.,” Rapid object detection using a boosted cascade of simple features”, In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, 1: I-I, (2001)
  • Chen, C., Seff, A., Kornhauser, A., & Xiao, J., “Deepdriving: Learning affordance for direct perception in autonomous driving”, In Proceedings of the IEEE international conference on computer vision, 2722-2730, (2015)
  • Chen, Y., Zhao, D., Lv, L., & Zhang, Q., “Multi-task learning for dangerous object detection in autonomous driving”, Information Sciences, 432:559-571, (2018)
  • Chen, X., Kundu, K., Zhang, Z., Ma, H., Fidler, S., & Urtasun, R., “Monocular 3d object detection for autonomous driving”, In Proceedings of the IEEE conference on computer vision and pattern recognition, 2147-2156, (2016)
  • Ren, S., He, K., Girshick, R., & Sun, J., “Faster r-cnn: Towards real-time object detection with region proposal networks”, Advances in neural information processing systems, 28:91-99, (2015)
  • Brunetti, A., Buongiorno, D., Trotta, G. F., & Bevilacqua, V., “Computer vision and deep learning techniques for pedestrian detection and tracking: A survey” Neurocomputing, 300:17-33, (2018)
  • Yang, B., Huang, C., & Nevatia, R., “Learning affinities and dependencies for multi-target tracking using a CRF model”, In CVPR 2011, 1233-1240, (2011)
  • Wojke, N., Bewley, A., & Paulus, D., “Simple online and realtime tracking with a deep association metric”, In 2017 IEEE international conference on image processing (ICIP), 3645-3649, (2017)
  • Yang, Z., & Nevatia, R., “A multi-scale cascade fully convolutional network face detector”, In 2016 23rd International Conference on Pattern Recognition (ICPR), 633-638, (2016)
  • Coşkun, M., Uçar, A., Yildirim, Ö., & Demir, Y., “Face recognition based on convolutional neural network”, In 2017 International Conference on Modern Electrical and Energy Systems (MEES), 376-379, (2017)
  • Kamencay, P., Benco, M., Mizdos, T., & Radil, R., “A new method for face recognition using convolutional neural network”, Advances in Electrical and Electronic Engineering, 15(4):663-672, (2017)
  • Salari, A., Djavadifar, A., Liu, X. R., & Najjaran, H., “Object recognition datasets and challenges: A review”, Neurocomputing, (2022)
  • Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., ... & Fei-Fei, L., “Imagenet large scale visual recognition challenge”, International journal of computer vision, 115(3):211-252, (2015)
  • Karagiannakos S., “Localization and Object Detection with Deep Learning”, Towards Data Science, (2019)
  • Stanford University, “Lecture 11: Detection and Segmentation”, Stanford University, (2019)
  • Gürbüz, M. E., & Gangal, A., “Döndürülmüş Kayan Pencereler Kullanarak İyleştirilmiş Hibrid Nesne Tespit Yöntemi “, Eleco 2014 Elektrik – Elektronik – Bilgisayar ve Biyomedikal Mühendisliği Sempozyumu, 573-577, (2014)
  • Lienhart, R., & Maydt, J., “An extended set of haar-like features for rapid object detection”, In Proceedings. international conference on image processing, 1: I-I, (2002)
  • Papageorgiou, C. P., Oren, M., & Poggio, T., “A general framework for object detection”, In Sixth International Conference on Computer Visio, 555-562, (1998)
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  • Redmon, J., Divvala, S., Girshick, R., & Farhadi, A., “You only look once: Unified, real-time object detection”, In Proceedings of the IEEE conference on computer vision and pattern recognition, 779-788, (2016)
  • Redmon, J., & Farhadi, A., “YOLO9000: better, faster, stronger.” In Proceedings of the IEEE conference on computer vision and pattern recognition, 7263-7271, (2017)
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  • Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S., “Feature pyramid networks for object detectio”, In Proceedings of the IEEE conference on computer vision and pattern recognition, 2117-2125, (2017)
  • Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M., “Yolov4: Optimal speed and accuracy of object detection”, arXiv e-prints, arXiv-2004, (2020)
  • Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J., “Path aggregation network for instance segmentation”, In Proceedings of the IEEE conference on computer vision and pattern recognition, 8759-8768, (2018)
  • Aly, G. H., Marey, M. A. E. R., El-Sayed Âmin, S., & Tolba, M. F.,” YOLO V3 and YOLO V4 for masses detection in mammograms with resnet and inception for masses classification”, In International Conference on Advanced Machine Learning Technologies and Applications, 145-153, (2021)
  • Sozzi, M., Cantalamessa, S., Cogato, A., Kayad, A., & Marinello, F.,” Automatic bunch detection in white grape varieties using YOLOv3, YOLOv4, and YOLOv5 deep learning algorithms”, Agronomy, 12(2):319, (2022)
  • Rahman, E. U., Zhang, Y., Ahmad, S., Ahmad, H. I., & Jobaer, S., “Autonomous vision-based primary distribution systems porcelain insulators inspection using UAVs”, Sensors, 21(3):974, (2021)
  • Ge, Z., Liu, S., Wang, F., Li, Z., & Sun, J., “Yolox: Exceeding yolo series in 2021”, arXiv preprint, (2021)
  • Nepal, U., & Eslamiat, H., “Comparing YOLOv3, YOLOv4 and YOLOv5 for autonomous landing spot detection in faulty UAVs”, Sensors, 22(2):464, (2022)
  • Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C., “Ssd: Single shot multibox detector”, In European conference on computer vision, 21-37, (2016)
  • Szegedy, C., Reed, S., Erhan, D., Anguelov, D., & Ioffe, S., “Scalable, high-quality object detection”, arXiv preprint, arXiv:1412.1441, (2014)
  • Uner, M. K., Ramac, L. C., Varshney, P. K., & Alford, M. G., “Concealed weapon detection: an image fusion approach”, In Investigative image processing, 2942:123-132. (1997)
  • Sheen, D. M., McMakin, D. L., & Hall, T. E., “Three-dimensional millimeter-wave imaging for concealed weapon detection”, IEEE Transactions on microwave theory and techniques, 49(9):1581-1592, (2001)
  • Xue, Z., Blum, R. S., & Li, Y. “Fusion of visual and IR images for concealed weapon detection”, In Proceedings of the Fifth International Conference on Information Fusion. FUSION 2002, 2:1198-1205, (2002)
  • Blum, R., Xue, Z., Liu, Z., & Forsyth, D. S., “Multisensor concealed weapon detection by using a multiresolution mosaic approach” In IEEE 60th Vehicular Technology Conference, 2004. 7:4597-4601, (2004)
  • Upadhyay, E. M., & Rana, N. K., “Exposure fusion for concealed weapon detection”, In 2014 2nd International Conference on Devices, Circuits and Systems (ICDCS), 1-6, (2014)
  • O'reilly, D., Bowring, N., & Harmer, S., “Signal processing techniques for concealed weapon detection by use of neural networks”, In 2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel, 1-4, (2012)
  • Darker, I. T., Gale, A. G., & Blechko, A., “CCTV as an automated sensor for firearms detection: Human-derived performance as a precursor to automatic recognition”, In Unmanned/Unattended Sensors and Sensor Networks V, 7112:208-219, (2008)
  • Grega, M., Łach, S., & Sieradzki, R., “Automated recognition of firearms in surveillance video”, In 2013 IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA), 45-50, (2013).
  • Tiwari, R. K., & Verma, G. K., “A computer vision based framework for visual gun detection using harris interest point detector”, Procedia Computer Science, 54:703-712, (2015).
  • Tiwari, R. K., & Verma, G. K., “A computer vision based framework for visual gun detection using SURF”, In 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO), 1-5, (2015).
  • Grega, M., Matiolański, A., Guzik, P., & Leszczuk, M., “Automated detection of firearms and knives in a CCTV image”, Sensors, 16(1):47, (2016).
  • Vajhala, R., Maddineni, R., & Yeruva, P. R., “Weapon detection in surveillance camera images”, Electrical Engineering, (2016).
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  • Pérez-Hernández, F., Tabik, S., Lamas, A., Olmos, R., Fujita, H., & Herrera, F., “Object detection binary classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance”, Knowledge-Based Systems, 194, (2020)
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  • Narejo, S., Pandey, B., Rodriguez, C., & Anjum, M. R., “Weapon detection using YOLO V3 for smart surveillance system”, Mathematical Problems in Engineering, (2021)
  • Hashmi, T. S. S., Haq, N. U., Fraz, M. M., & Shahzad, M., “Application of Deep Learning for Weapons Detection in Surveillance Videos”, In 2021 International Conference on Digital Futures and Transformative Technologies (ICoDT2), 1-6, (2021)
  • Kayalvizhi, R., Malarvizhi, S., Choudhury, S. D., Topkar, A., & Vijayakumar, P., “Detection of sharp objects using deep neural network based object detection algorithm”, In 2020 4th International Conference on Computer, Communication and Signal Processing (ICCCSP), 1-5, (2020)
  • Bhatti, M. T., Khan, M. G., Aslam, M., & Fiaz, M. J., “Weapon detection in real-time cctv videos using deep learning”, IEEE Access, 9:34366-34382, (2021)
  • Salido, J., Lomas, V., Ruiz-Santaquiteria, J., & Deniz, O., “Automatic handgun detection with deep learning in video surveillance images”, Applied Sciences, 11(13):6085, (2021)
  • Iqbal, M. J., Iqbal, M. M., Ahmad, I., Alassafi, M. O., Alfakeeh, A. S., & Alhomoud, A. “Real-Time Surveillance Using Deep Learning”, Security and Communication Networks, (2021)
  • Sivakumar, P., “Real Time Crime Detection Using Deep Learning Algorithm”, In 2021 International Conference on System, Computation, Automation and Networking (ICSCAN), 1-5, (2021)
  • Kaya, V., Tuncer, S., & Baran, A., “Detection and classification of different weapon types using deep learning”, Applied Sciences, 11(16):7535, (2021)
  • Bushra, S. N., Shobana, G., Maheswari, K. U., & Subramanian, N., “Smart Video Survillance Based Weapon Identification Using Yolov5”, In 2022 International Conference on Electronic Systems and Intelligent Computing (ICESIC), 351-357, (2022)
Toplam 101 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler(Derleme)
Yazarlar

Ayşe Berika Varol Malkoçoğlu 0000-0003-1856-9636

Rüya Şamlı 0000-0002-8723-1228

Erken Görünüm Tarihi 29 Haziran 2023
Yayımlanma Tarihi 29 Haziran 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 16 Sayı: 1

Kaynak Göster

APA Varol Malkoçoğlu, A. B., & Şamlı, R. (2023). Video Verilerinde Bulunan Tehlikeli Nesnelerin Derin Öğrenme Yöntemleri ile Tespiti Üzerine Derleme. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, 16(1), 60-80. https://doi.org/10.54525/tbbmd.1184322
AMA Varol Malkoçoğlu AB, Şamlı R. Video Verilerinde Bulunan Tehlikeli Nesnelerin Derin Öğrenme Yöntemleri ile Tespiti Üzerine Derleme. TBV-BBMD. Haziran 2023;16(1):60-80. doi:10.54525/tbbmd.1184322
Chicago Varol Malkoçoğlu, Ayşe Berika, ve Rüya Şamlı. “Video Verilerinde Bulunan Tehlikeli Nesnelerin Derin Öğrenme Yöntemleri Ile Tespiti Üzerine Derleme”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi 16, sy. 1 (Haziran 2023): 60-80. https://doi.org/10.54525/tbbmd.1184322.
EndNote Varol Malkoçoğlu AB, Şamlı R (01 Haziran 2023) Video Verilerinde Bulunan Tehlikeli Nesnelerin Derin Öğrenme Yöntemleri ile Tespiti Üzerine Derleme. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 16 1 60–80.
IEEE A. B. Varol Malkoçoğlu ve R. Şamlı, “Video Verilerinde Bulunan Tehlikeli Nesnelerin Derin Öğrenme Yöntemleri ile Tespiti Üzerine Derleme”, TBV-BBMD, c. 16, sy. 1, ss. 60–80, 2023, doi: 10.54525/tbbmd.1184322.
ISNAD Varol Malkoçoğlu, Ayşe Berika - Şamlı, Rüya. “Video Verilerinde Bulunan Tehlikeli Nesnelerin Derin Öğrenme Yöntemleri Ile Tespiti Üzerine Derleme”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri ve Mühendisliği Dergisi 16/1 (Haziran 2023), 60-80. https://doi.org/10.54525/tbbmd.1184322.
JAMA Varol Malkoçoğlu AB, Şamlı R. Video Verilerinde Bulunan Tehlikeli Nesnelerin Derin Öğrenme Yöntemleri ile Tespiti Üzerine Derleme. TBV-BBMD. 2023;16:60–80.
MLA Varol Malkoçoğlu, Ayşe Berika ve Rüya Şamlı. “Video Verilerinde Bulunan Tehlikeli Nesnelerin Derin Öğrenme Yöntemleri Ile Tespiti Üzerine Derleme”. Türkiye Bilişim Vakfı Bilgisayar Bilimleri Ve Mühendisliği Dergisi, c. 16, sy. 1, 2023, ss. 60-80, doi:10.54525/tbbmd.1184322.
Vancouver Varol Malkoçoğlu AB, Şamlı R. Video Verilerinde Bulunan Tehlikeli Nesnelerin Derin Öğrenme Yöntemleri ile Tespiti Üzerine Derleme. TBV-BBMD. 2023;16(1):60-8.
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