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Güvenlik Kameralarında Otomatik Silah ve Bıçak Tespit Sistemi: Karşılaştırmalı YOLO Modelleri

Year 2022, Issue: 41, 16 - 22, 30.11.2022
https://doi.org/10.31590/ejosat.1163675

Abstract

Güvenlik kamera sistemleri genellikle sosyal ve kamusal alanlarda güvenli ortam koşulları oluşturmak için kullanılmaktadır. Günümüz güvenlik kamerası sistemlerinde insan görüşü yerine yapay zeka tabanlı bilgisayar görüsünden yararlanılmaya başlanılmıştır. Bu çalışma da toplumsal ve kamusal alanlarda olabilecek silah ve bıçak görüntülerinin bilgisayarlı görü ile görülüp tespit edilmesi amaçlanmaktadır. Çalışmada yöntem ve model olarak, görüntü işleme teknolojisi ve genellikle literatürde oldukça başarılı sonuçlar elde edildiği bilgisinin mevcut olan YOLO algoritmaları kullanılmıştır. YOLO algoritmalarından YOLOv4, YOLOv5, YOLOR ve YOLOX modelleri kullanılmıştır. Çalışmada veri seti olarak 5078 görüntü kullanılmış ve bu görüntülerin 3000 adetini silah ve 2078 adetini bıçak görüntüleri oluştumaktadır. Görüntülerden elde edilecek deneysel çalışma için güvenilirliğin sağlanması için görüntülerin seçiciliğinin zor olmasına dikkat edilmiştir. YOLO algoritmalarının karşılaştırmalı deneysel çalışmaları yapılmış ve sonuçları yayınlanmıştır. Görüntülerde silah ve bıçak tespitinde en başarılı sonuç %97,6 map@0,5 değeri ile YOLOR modelinde elde edilmiştir.

References

  • Velastin, S. A., Boghossian, B. A., & Vicencio-Silva, M. A. (2006). A motion-based image processing system for detecting potentially dangerous situations in underground railway stations. Transportation Research Part C: Emerging Technologies, 14(2), 96-113.
  • Bilgin, R. (2014). Çatışma Ve Şiddet Ortamında Büyüyen Çocuklar Sorunu. Fırat Üniversitesi Sosyal Bilimler Dergisi, 24(1), 135-152.
  • Kumar, P. M., Gandhi, U., Varatharajan, R., Manogaran, G., Jidhesh, R., & Vadivel, T. (2019). Intelligent face recognition and navigation system using neural learning for smart security in Internet of Things. Cluster Computing, 22(4), 7733-7744.
  • Babanne, V., Mahajan, N. S., Sharma, R. L., & Gargate, P. P. (2019, December). Machine learning based smart surveillance system. In 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 84-86). IEEE.
  • Joshi, A., Jagdale, N., Gandhi, R., & Chaudhari, S. (2019, June). Smart surveillance system for detection of suspicious behaviour using machine learning. In International Conference on Intelligent Computing, Information and Control Systems (pp. 239-248). Springer, Cham.
  • Ko, K. E., & Sim, K. B. (2018). Deep convolutional framework for abnormal behavior detection in a smart surveillance system. Engineering Applications of Artificial Intelligence, 67, 226-234.
  • Warsi, A., Abdullah, M., Husen, M. N., Yahya, M., Khan, S., & Jawaid, N. (2019, August). Gun detection system using YOLOv3. In 2019 IEEE International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) (pp. 1-4). IEEE.
  • Narejo, S., Pandey, B., Rodriguez, C., & Anjum, M. R. (2021). Weapon Detection Using YOLO V3 for Smart Surveillance System. Mathematical Problems in Engineering, 2021.
  • Ashraf, A. H., Imran, M., Qahtani, A. M., Alsufyani, A., Almutiry, O., Mahmood, A., ... & Habib, M. (2022). Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s. CMC-COMPUTERS MATERIALS & CONTINUA, 70(2), 2761-2775.
  • AKBULUT, Y., & KHALAF, R. Smart Arms Detection System Using YOLO Algorithm and OpenCV Libraries. Turkish Journal of Science and Technology, 16(1), 129-136.
  • Duran-Vega, M. A., Gonzalez-Mendoza, M., Chang-Fernandez, L., & Suarez-Ramirez, C. D. (2021). TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in Video. arXiv preprint arXiv:2111.08867.
  • Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
  • GÜÇLÜ, E., AYDIN, İ., ŞAHBAZ, K., Erhan, A. K. I. N., & KARAKÖSE, M. (2021). Demiryolu Bağlantı Elemanlarında Bulunan Kusurların YOLOv4 ve Bulanık Mantık Kullanarak Tespiti. Demiryolu Mühendisliği, (14), 249-262.
  • Wang, C. Y., Liao, H. Y. M., Wu, Y. H., Chen, P. Y., Hsieh, J. W., & Yeh, I. H. (2020). CSPNet: A new backbone that can enhance learning capability of CNN. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops (pp. 390-391).
  • Woo, S., Park, J., Lee, J. Y., & Kweon, I. S. (2018). Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV) (pp. 3-19).
  • He, K., Zhang, X., Ren, S., & Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 37(9), 1904-1916.
  • Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018). Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8759-8768).
  • Murat, S. (2021). İnsansız hava aracı görüntülerinden derin öğrenme yöntemleriyle nesne tanıma (Master's thesis, Maltepe Üniversitesi, Lisansüstü Eğitim Enstitüsü).
  • Zhang, S., Song, L., Liu, S., Ge, Z., Li, Z., He, X., & Sun, J. (2021). Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge. arXiv preprint arXiv:2108.04230.
  • Ge, Z., Liu, S., Wang, F., Li, Z., & Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430.
  • Wang, C. Y., Yeh, I. H., & Liao, H. Y. M. (2021). You Only Learn One Representation: Unified Network for Multiple Tasks. arXiv preprint arXiv:2105.04206.
  • Olmos, R., Tabik, S., & Herrera, F. (2018) Automatic handgun detection alarm in videos using deep learning. Neurocomputing, 275, 66-72. doi.org/10.1016/j.neucom.2017.05.012
  • Castillo, A., Tabik, S., Pérez, F., Olmos, R., Herrera, F. (2019) Brightness guided preprocessing for automatic cold steel weapon detection in surveillance videos using deep learning. Neurocomputing, 330, 151-161. doi.org/10.1016/j.neucom.2018.10.076

Automatic Weapon and Knife Detection System on Security Cameras: Comparative YOLO Models

Year 2022, Issue: 41, 16 - 22, 30.11.2022
https://doi.org/10.31590/ejosat.1163675

Abstract

Security camera systems are generally used to create safe environment conditions in social and public areas. In today’s security camera systems, artificial intelligence-based computer vision has started to be used instead of human vision. In this study, it is aimed to see and detect images of guns and knives that may be in social and public spaces with computer vision. As a method and model in the study, image processing technology and YOLO algorithms, which are generally known to have very successful results in the literature, were used. Among the YOLO algorithms, YOLOv4, YOLOv5, YOLOR and YOLOX models were used. 5078 images were used as a data set in the study, and 3000 of these images consist of images of weapons and 2078 of knife images. In order to ensure reliability for the experimental study to be obtained from the images, attention has been paid to the fact that the selectivity of the images is difficult. Comparative experimental studies of YOLO algorithms have been carried out and their results have been published. The most successful result in detecting weapons and knives in the images was obtained in the YOLOR model with a map@0,5 value of 97.6%.

References

  • Velastin, S. A., Boghossian, B. A., & Vicencio-Silva, M. A. (2006). A motion-based image processing system for detecting potentially dangerous situations in underground railway stations. Transportation Research Part C: Emerging Technologies, 14(2), 96-113.
  • Bilgin, R. (2014). Çatışma Ve Şiddet Ortamında Büyüyen Çocuklar Sorunu. Fırat Üniversitesi Sosyal Bilimler Dergisi, 24(1), 135-152.
  • Kumar, P. M., Gandhi, U., Varatharajan, R., Manogaran, G., Jidhesh, R., & Vadivel, T. (2019). Intelligent face recognition and navigation system using neural learning for smart security in Internet of Things. Cluster Computing, 22(4), 7733-7744.
  • Babanne, V., Mahajan, N. S., Sharma, R. L., & Gargate, P. P. (2019, December). Machine learning based smart surveillance system. In 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC) (pp. 84-86). IEEE.
  • Joshi, A., Jagdale, N., Gandhi, R., & Chaudhari, S. (2019, June). Smart surveillance system for detection of suspicious behaviour using machine learning. In International Conference on Intelligent Computing, Information and Control Systems (pp. 239-248). Springer, Cham.
  • Ko, K. E., & Sim, K. B. (2018). Deep convolutional framework for abnormal behavior detection in a smart surveillance system. Engineering Applications of Artificial Intelligence, 67, 226-234.
  • Warsi, A., Abdullah, M., Husen, M. N., Yahya, M., Khan, S., & Jawaid, N. (2019, August). Gun detection system using YOLOv3. In 2019 IEEE International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) (pp. 1-4). IEEE.
  • Narejo, S., Pandey, B., Rodriguez, C., & Anjum, M. R. (2021). Weapon Detection Using YOLO V3 for Smart Surveillance System. Mathematical Problems in Engineering, 2021.
  • Ashraf, A. H., Imran, M., Qahtani, A. M., Alsufyani, A., Almutiry, O., Mahmood, A., ... & Habib, M. (2022). Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s. CMC-COMPUTERS MATERIALS & CONTINUA, 70(2), 2761-2775.
  • AKBULUT, Y., & KHALAF, R. Smart Arms Detection System Using YOLO Algorithm and OpenCV Libraries. Turkish Journal of Science and Technology, 16(1), 129-136.
  • Duran-Vega, M. A., Gonzalez-Mendoza, M., Chang-Fernandez, L., & Suarez-Ramirez, C. D. (2021). TYolov5: A Temporal Yolov5 Detector Based on Quasi-Recurrent Neural Networks for Real-Time Handgun Detection in Video. arXiv preprint arXiv:2111.08867.
  • Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788).
  • GÜÇLÜ, E., AYDIN, İ., ŞAHBAZ, K., Erhan, A. K. I. N., & KARAKÖSE, M. (2021). Demiryolu Bağlantı Elemanlarında Bulunan Kusurların YOLOv4 ve Bulanık Mantık Kullanarak Tespiti. Demiryolu Mühendisliği, (14), 249-262.
  • Wang, C. Y., Liao, H. Y. M., Wu, Y. H., Chen, P. Y., Hsieh, J. W., & Yeh, I. H. (2020). CSPNet: A new backbone that can enhance learning capability of CNN. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops (pp. 390-391).
  • Woo, S., Park, J., Lee, J. Y., & Kweon, I. S. (2018). Cbam: Convolutional block attention module. In Proceedings of the European conference on computer vision (ECCV) (pp. 3-19).
  • He, K., Zhang, X., Ren, S., & Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 37(9), 1904-1916.
  • Liu, S., Qi, L., Qin, H., Shi, J., & Jia, J. (2018). Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8759-8768).
  • Murat, S. (2021). İnsansız hava aracı görüntülerinden derin öğrenme yöntemleriyle nesne tanıma (Master's thesis, Maltepe Üniversitesi, Lisansüstü Eğitim Enstitüsü).
  • Zhang, S., Song, L., Liu, S., Ge, Z., Li, Z., He, X., & Sun, J. (2021). Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge. arXiv preprint arXiv:2108.04230.
  • Ge, Z., Liu, S., Wang, F., Li, Z., & Sun, J. (2021). Yolox: Exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430.
  • Wang, C. Y., Yeh, I. H., & Liao, H. Y. M. (2021). You Only Learn One Representation: Unified Network for Multiple Tasks. arXiv preprint arXiv:2105.04206.
  • Olmos, R., Tabik, S., & Herrera, F. (2018) Automatic handgun detection alarm in videos using deep learning. Neurocomputing, 275, 66-72. doi.org/10.1016/j.neucom.2017.05.012
  • Castillo, A., Tabik, S., Pérez, F., Olmos, R., Herrera, F. (2019) Brightness guided preprocessing for automatic cold steel weapon detection in surveillance videos using deep learning. Neurocomputing, 330, 151-161. doi.org/10.1016/j.neucom.2018.10.076
There are 23 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Mehmet Tevfik Ağdaş 0000-0002-5608-6240

Sevinç Gülseçen 0000-0001-8537-7111

Early Pub Date October 2, 2022
Publication Date November 30, 2022
Published in Issue Year 2022 Issue: 41

Cite

APA Ağdaş, M. T., & Gülseçen, S. (2022). Güvenlik Kameralarında Otomatik Silah ve Bıçak Tespit Sistemi: Karşılaştırmalı YOLO Modelleri. Avrupa Bilim Ve Teknoloji Dergisi(41), 16-22. https://doi.org/10.31590/ejosat.1163675