Araştırma Makalesi
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Yıl 2021, Cilt 4, Sayı 1, 131 - 141, 30.04.2021
https://doi.org/10.35377/saucis.04.01.891308

Öz

Kaynakça

  • “Umut Vakfı - Türkiye Silahlı Şiddet Haritası 2019”. [Online]. Available: http://umut.org.tr/umut-vakfi-turkiye-silahli-siddet-haritasi-2019/ [Accessed: 02-January-2021].
  • S. B. Kibria and M. S. Hasan, “An analysis of Feature extraction and Classification Algorithms for Dangerous Object Detection,” In 2017 2nd International Conference on Electrical & Electronic Engineering (ICEEE), pp. 1-4, 2017.
  • R. Kanehisa and A. Neto, “Firearm Detection using Convolutional Neural Networks”, [Online]. Available: https://www.scitepress.org/Link.aspx?doi=10.5220/0007397707070714/ [Accessed: 24-January-2021].
  • M. Grega, A. Matiolański, P. Guzik, and M. Leszczuk, “Automated Detection of Firearms and Knives in a CCTV Image,” Sensors, vol. 16, no 1, pp. 47, 2016.
  • J. L. Salazar González, C. Zaccaro, J. A. Álvarez-García, L. M. Soria Morillo, and F. Sancho Caparrini, “Real-time gun detection in CCTV: An open problem,” Neural networks, vol. 132, pp. 297-308, 2020.
  • G. K. Verma and A. Dhillon, “A Handheld Gun Detection using Faster R-CNN Deep Learning,” Proceedings of the 7th International Conference on Computer and Communication Technology, New York, NY, USA, pp. 84-88, 2017.
  • R. K. Tiwari, and G. K. Verma, “A computer vision based framework for visual gun detection using harris interest point detector,” Procedia Computer Science, vol. 54, pp. 703-712, 2015.
  • M. Kmieć, A. Głowacz, and A. Dziech, “Towards robust visual knife detection in images: active appearance models initialised with shape-specific interest points,” In International Conference on Multimedia Communications, Services and Security, Berlin, Heidelberg, 2012, pp. 148-158, doi: 10.1007/978-3-642-30721-8_15.
  • Castillo, S. Tabik, F. Pérez, R. Olmos, and F. Herrera, “Brightness guided preprocessing for automatic cold steel weapon detection in surveillance videos with deep learning,” Neurocomputing, vol. 330, pp. 151-161, 2019.
  • M. M. Fernandez-Carrobles, O. Deniz, and F. Maroto, “Gun and Knife Detection Based on Faster R-CNN for Video Surveillance,” Pattern Recognition and Image Analysis, Cham, pp. 441-452, 2019.
  • H. Jain, A. Vikram, Mohana, A. Kashyap, and A. Jain, “Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications,” 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 193-198, 2020.
  • R. Olmos, S. Tabik, and F. Herrera, “Automatic handgun detection alarm in videos using deep learning,” Neurocomputing, vol. 275, pp. 66-72, 2018.
  • R. Olmos, S. Tabik, A. Lamas, F. Pérez-Hernández, and F. Herrera, “A binocular image fusion approach for minimizing false positives in handgun detection with deep learning,” Inf. Fusion, no. 49, pp. 271-280, 2019.
  • F. Pérez-Hernández, S. Tabik, A. Lamas, R. Olmos, H. Fujita, and F. Herrera, “Object Detection Binary Classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance,” Knowl.-Based Syst., vol. 194, pp. 105590, 2020.
  • A. Matiolański, A. Maksimova, and A. Dziech, “CCTV object detection with fuzzy classification and image enhancement,” Multimed. Tools Appl., vol. 75, no. 17, pp. 10513-10528, 2016.
  • M. Türkoğlu, K. Hanbay, I. S. Sivrikaya, and D. Hanbay, “Derin Evrişimsel Sinir Ağı Kullanılarak Kayısı Hastalıklarının Sınıflandırılması,” Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 9, no. 1, pp. 334-345, 2021.
  • “Introduction to Convolutional Neural Networks”, 2018. [Online]. Available: https://rubikscode.net/2018/02/26/introduction-to-convolutional-neural-networks/ [Accessed: 03-January-2021].
  • M. Turkoglu, “COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble,” Applied Intelligence, pp. 1-14, 2020.
  • K. Kayaalp, and S. Metlek, “Classification of robust and rotten apples by deep learning algorithm,” Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 2, pp. 112-120, 2020.
  • I. Ozsahin, and D. U. Ozsahin, “Neural network applications in medicine,” In Biomedical Signal Processing and Artificial Intelligence in Healthcare, Academic Press, pp. 183-206, 2020.
  • M. Türkoğlu, and D. Hanbay, “Plant disease and pest detection using deep learning-based features,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 27, no. 3, pp. 1636-1651, 2019.
  • H. Ahmetoğlu, and R. Daş, “Derin Öğrenme ile Büyük Veri Kumelerinden Saldırı Türlerinin Sınıflandırılması,” In 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1-9, 2019,
  • R. Daş, B. Polat, and G. Tuna, “Derin Öğrenme ile Resim ve Videolarda Nesnelerin Tanınması ve Takibi,” Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 31, no. 2, pp. 571-581, 2019.
  • D. Şengür, and S. Siuly, “Efficient approach for EEG-based emotion recognition,” Electronics Letters, vol. 56, no. 25, pp.1361-1364, 2020.
  • A. Krizhevsk, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” In: Advances in Neural Information Processing Systems, pp. 1097-1105, 2012.
  • F. Doğan, and I. Türkoğlu, “Comparison of Leaf Classification Performance of Deep Learning Algorithms,” Sakarya University Journal of Computer and Information Sciences, vol. 1, pp. 10-21, 2018.
  • M. Uçar, and E. Uçar, “Computer-aided detection of lung nodules in chest X-rays using deep convolutional neural networks,” Sakarya University Journal of Computer and Information Sciences, vol. 2, no. 1, pp. 41-52, 2019.
  • K. Simonyan, and A. Zisserman “Very deep convolutional networks for large-scale image recognition,” arXiv preprint, arXiv:1409.1556, 2014.
  • D. Akgün, “An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images,” Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 3, pp. 264-271, 2020.
  • E. Erdem, and T. Aydın, “Detection of Pneumonia with a Novel CNN-based Approach,” Sakarya University Journal of Computer and Information Sciences, vol. 4, no. 1, pp. 26-34, 2021.
  • M. Turkoglu, O. F. Alcin, M. Aslan, A. Al-Zebari, and A. Sengur, “Deep rhythm and long short term memory-based drowsiness detection.,” Biomedical Signal Processing and Control, vol. 65, pp. 102364, 2021.
  • M. U. Salur, İ. Aydın, and M. Karaköse, “Gömülü Derin Öğrenme ile Tehdit İçeren Nesnelerin Gerçek Zamanda Tespiti,” Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, vol. 10, no. 2, pp. 497-509, 2019.
  • D. Mery, E. Svec, M. Arias, V. Riffo, J. M. Saavedra, and S. Banerjee, “Modern computer vision techniques for x-ray testing in baggage inspection,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 4, pp. 682-692, 2016.
  • N. Dwivedi, D. K. Singh, and D. S. Kushwaha, “Weapon Classification using Deep Convolutional Neural Network,” In 2019 IEEE Conference on Information and Communication Technology, pp. 1-5, 2019.

Deep Neural Networks Based on Transfer Learning Approaches to Classification of Gun and Knife Images

Yıl 2021, Cilt 4, Sayı 1, 131 - 141, 30.04.2021
https://doi.org/10.35377/saucis.04.01.891308

Öz

Most of the criminal acts are performed using criminal tools. One of the most effective ways of preventing crime is to observe and detect offensive weapons by security camera systems. Deep learning techniques can show very high-performance in observing and perceiving objects. In the current study, the performances of the pre-trained AlexNet, VGG16, and VGG19 models based on convolutional neural networks, were tested for the detection and classification of criminal tools such as guns and knives. In the study, the training process was carried out using transfer learning approaches such as Fine-tuning and Training from scratch based on deep architectures. To test the deep architectures used in the proposed study, the gun and knife datasets frequently used in the literature were collected and combined with new datasets obtained originally from search engines and videos, and then their performances were tested. In the experimental results, the VGG16 model based on fine-tuning for the two and three classes achieved the highest accuracy in detecting criminal devices with a rate of 99.73% and 99.67%, respectively. As a result, the study has observed that offensive weapons could be detected with security cameras using learned weights of deep architectures

Kaynakça

  • “Umut Vakfı - Türkiye Silahlı Şiddet Haritası 2019”. [Online]. Available: http://umut.org.tr/umut-vakfi-turkiye-silahli-siddet-haritasi-2019/ [Accessed: 02-January-2021].
  • S. B. Kibria and M. S. Hasan, “An analysis of Feature extraction and Classification Algorithms for Dangerous Object Detection,” In 2017 2nd International Conference on Electrical & Electronic Engineering (ICEEE), pp. 1-4, 2017.
  • R. Kanehisa and A. Neto, “Firearm Detection using Convolutional Neural Networks”, [Online]. Available: https://www.scitepress.org/Link.aspx?doi=10.5220/0007397707070714/ [Accessed: 24-January-2021].
  • M. Grega, A. Matiolański, P. Guzik, and M. Leszczuk, “Automated Detection of Firearms and Knives in a CCTV Image,” Sensors, vol. 16, no 1, pp. 47, 2016.
  • J. L. Salazar González, C. Zaccaro, J. A. Álvarez-García, L. M. Soria Morillo, and F. Sancho Caparrini, “Real-time gun detection in CCTV: An open problem,” Neural networks, vol. 132, pp. 297-308, 2020.
  • G. K. Verma and A. Dhillon, “A Handheld Gun Detection using Faster R-CNN Deep Learning,” Proceedings of the 7th International Conference on Computer and Communication Technology, New York, NY, USA, pp. 84-88, 2017.
  • R. K. Tiwari, and G. K. Verma, “A computer vision based framework for visual gun detection using harris interest point detector,” Procedia Computer Science, vol. 54, pp. 703-712, 2015.
  • M. Kmieć, A. Głowacz, and A. Dziech, “Towards robust visual knife detection in images: active appearance models initialised with shape-specific interest points,” In International Conference on Multimedia Communications, Services and Security, Berlin, Heidelberg, 2012, pp. 148-158, doi: 10.1007/978-3-642-30721-8_15.
  • Castillo, S. Tabik, F. Pérez, R. Olmos, and F. Herrera, “Brightness guided preprocessing for automatic cold steel weapon detection in surveillance videos with deep learning,” Neurocomputing, vol. 330, pp. 151-161, 2019.
  • M. M. Fernandez-Carrobles, O. Deniz, and F. Maroto, “Gun and Knife Detection Based on Faster R-CNN for Video Surveillance,” Pattern Recognition and Image Analysis, Cham, pp. 441-452, 2019.
  • H. Jain, A. Vikram, Mohana, A. Kashyap, and A. Jain, “Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications,” 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), pp. 193-198, 2020.
  • R. Olmos, S. Tabik, and F. Herrera, “Automatic handgun detection alarm in videos using deep learning,” Neurocomputing, vol. 275, pp. 66-72, 2018.
  • R. Olmos, S. Tabik, A. Lamas, F. Pérez-Hernández, and F. Herrera, “A binocular image fusion approach for minimizing false positives in handgun detection with deep learning,” Inf. Fusion, no. 49, pp. 271-280, 2019.
  • F. Pérez-Hernández, S. Tabik, A. Lamas, R. Olmos, H. Fujita, and F. Herrera, “Object Detection Binary Classifiers methodology based on deep learning to identify small objects handled similarly: Application in video surveillance,” Knowl.-Based Syst., vol. 194, pp. 105590, 2020.
  • A. Matiolański, A. Maksimova, and A. Dziech, “CCTV object detection with fuzzy classification and image enhancement,” Multimed. Tools Appl., vol. 75, no. 17, pp. 10513-10528, 2016.
  • M. Türkoğlu, K. Hanbay, I. S. Sivrikaya, and D. Hanbay, “Derin Evrişimsel Sinir Ağı Kullanılarak Kayısı Hastalıklarının Sınıflandırılması,” Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 9, no. 1, pp. 334-345, 2021.
  • “Introduction to Convolutional Neural Networks”, 2018. [Online]. Available: https://rubikscode.net/2018/02/26/introduction-to-convolutional-neural-networks/ [Accessed: 03-January-2021].
  • M. Turkoglu, “COVIDetectioNet: COVID-19 diagnosis system based on X-ray images using features selected from pre-learned deep features ensemble,” Applied Intelligence, pp. 1-14, 2020.
  • K. Kayaalp, and S. Metlek, “Classification of robust and rotten apples by deep learning algorithm,” Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 2, pp. 112-120, 2020.
  • I. Ozsahin, and D. U. Ozsahin, “Neural network applications in medicine,” In Biomedical Signal Processing and Artificial Intelligence in Healthcare, Academic Press, pp. 183-206, 2020.
  • M. Türkoğlu, and D. Hanbay, “Plant disease and pest detection using deep learning-based features,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 27, no. 3, pp. 1636-1651, 2019.
  • H. Ahmetoğlu, and R. Daş, “Derin Öğrenme ile Büyük Veri Kumelerinden Saldırı Türlerinin Sınıflandırılması,” In 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1-9, 2019,
  • R. Daş, B. Polat, and G. Tuna, “Derin Öğrenme ile Resim ve Videolarda Nesnelerin Tanınması ve Takibi,” Fırat Üniversitesi Mühendislik Bilimleri Dergisi, vol. 31, no. 2, pp. 571-581, 2019.
  • D. Şengür, and S. Siuly, “Efficient approach for EEG-based emotion recognition,” Electronics Letters, vol. 56, no. 25, pp.1361-1364, 2020.
  • A. Krizhevsk, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” In: Advances in Neural Information Processing Systems, pp. 1097-1105, 2012.
  • F. Doğan, and I. Türkoğlu, “Comparison of Leaf Classification Performance of Deep Learning Algorithms,” Sakarya University Journal of Computer and Information Sciences, vol. 1, pp. 10-21, 2018.
  • M. Uçar, and E. Uçar, “Computer-aided detection of lung nodules in chest X-rays using deep convolutional neural networks,” Sakarya University Journal of Computer and Information Sciences, vol. 2, no. 1, pp. 41-52, 2019.
  • K. Simonyan, and A. Zisserman “Very deep convolutional networks for large-scale image recognition,” arXiv preprint, arXiv:1409.1556, 2014.
  • D. Akgün, “An Evaluation of VGG16 Binary Classifier Deep Neural Network for Noise and Blur Corrupted Images,” Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 3, pp. 264-271, 2020.
  • E. Erdem, and T. Aydın, “Detection of Pneumonia with a Novel CNN-based Approach,” Sakarya University Journal of Computer and Information Sciences, vol. 4, no. 1, pp. 26-34, 2021.
  • M. Turkoglu, O. F. Alcin, M. Aslan, A. Al-Zebari, and A. Sengur, “Deep rhythm and long short term memory-based drowsiness detection.,” Biomedical Signal Processing and Control, vol. 65, pp. 102364, 2021.
  • M. U. Salur, İ. Aydın, and M. Karaköse, “Gömülü Derin Öğrenme ile Tehdit İçeren Nesnelerin Gerçek Zamanda Tespiti,” Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, vol. 10, no. 2, pp. 497-509, 2019.
  • D. Mery, E. Svec, M. Arias, V. Riffo, J. M. Saavedra, and S. Banerjee, “Modern computer vision techniques for x-ray testing in baggage inspection,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 4, pp. 682-692, 2016.
  • N. Dwivedi, D. K. Singh, and D. S. Kushwaha, “Weapon Classification using Deep Convolutional Neural Network,” In 2019 IEEE Conference on Information and Communication Technology, pp. 1-5, 2019.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Bilimleri, Yapay Zeka
Bölüm Makaleler
Yazarlar

Mehmet Tevfik AĞDAŞ
ARTVIN CORUH UNIVERSITY
0000-0002-5608-6240
Türkiye


Muammer TÜRKOĞLU (Sorumlu Yazar)
BİNGÖL ÜNİVERSİTESİ
0000-0002-2377-4979
Türkiye


Sevinç GÜLSEÇEN
İSTANBUL ÜNİVERSİTESİ
0000-0001-8537-7111
Türkiye

Yayımlanma Tarihi 30 Nisan 2021
Başvuru Tarihi 4 Mart 2021
Kabul Tarihi 16 Mart 2021
Yayınlandığı Sayı Yıl 2021, Cilt 4, Sayı 1

Kaynak Göster

IEEE M. T. Ağdaş , M. Türkoğlu ve S. Gülseçen , "Deep Neural Networks Based on Transfer Learning Approaches to Classification of Gun and Knife Images", Sakarya University Journal of Computer and Information Sciences, c. 4, sayı. 1, ss. 131-141, Nis. 2021, doi:10.35377/saucis.04.01.891308