Öz
This study presents an effective and innovative solution to overcome the security issues/problems in public places. As an alternative video surveillance system, the proposed method detects and localizes gun and knife objects from videos in real-time. In connection with Convolutional Neural Network (CNN) based object detection, the Faster R-CNN structure was applied to detect gun and knife objects with the highest performance. After conducting a simulation on test images, we have found that the F1-score performance of the developed system is about 70% recognition rates. The trained Faster R-CNN model can be utilized for different public places, including airplanes, bus stations, stadiums, and public vehicles, where the security is an important factor. Moreover, the developed method can be embedded in the local surveillance system in terms of reporting dangerous objects as well as minimizing the risks caused by such objects.
Anahtar Kelimeler
Gun Detection Knife Detection Faster-Regions with Convolutional Neural Networks Local Surveillance System
Kaynakça
- Buckchash H. and Raman B., 2017, A robust object detector: application to detection of visual knives, International Conference on Multimedia & Expo Workshops, IEEE2017, 633-638.
- Girshick R., Donahue J., Darrell T. and Malik J., 2014. Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 580-587.
- Matiolański A., Maksimova A. and Dziech A., 2016. CCTV object detection with fuzzy classification and image enhancement, Multimedia Tools Applications, 75, 10513-10528.
- Ren S., He K., Girshick R. and Sun J., 2015. Faster R-CNN: Towards real-time object detection with region proposal networks, Advances in Neural Information Processing Systems, 91-99.
- Tiwari R.K. and Verma G.K., 2015. A computer vision based framework for visual gun detection using harris interest point detector, Procedia Computer Science, 54, 703-712.
- Yuenyong S., Hnoohom N. and Wongpatikaseree K., 2018. Automatic detection of knives in infrared images, Int. ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, IEEE2018, 65-68.
- 1- bbc.com, https://www.bbc.com/turkce/haberler-dunya-45195900, (05-06-2021).
- 2- everytownresearch.org, https://everytownresearch. org/gun-violence-america/, (05-06-2021).
- 3- Hurriyet.com, http://www.hurriyet.com.tr/kelebek/ saglik/her-yil-3-bin-kisi-silahla-oluyor-27289543, (05-06-2021).
- 4- labelimg, https://github.com/tzutalin/labelImg, (05-06-2021).
- 5- nytimes.com, https://www.nytimes.com/2018/12/18 /us/gun-deaths.html, (05-06-2021).
- 6- weapons-dataset, https://sci2s.ugr.es/weapons-detection, (05-06-2021).
- 7. youtube.com, https://www.youtube.com/watch?v= qV6hy28C8-8, (05-06-2021).
Öz
Bu çalışma, halka açık yerlerde güvenlik sorunlarının üstesinden gelmek için etkili ve yenilikçi bir çözüm sunmaktadır. Alternatif bir video gözetim sistemi olarak, önerilen yöntem videolardan silah ve bıçak nesnelerini gerçek zamanlı olarak algılar ve yerelleştirir. Evrişimsel Sinir Ağı tabanlı nesne algılama ile bağlantılı olarak, en yüksek performansa sahip silah ve bıçak nesnelerini tespit etmek için Hızlı-Bölgesel Tabanlı Evrişimsel Sinir Ağı yapısı uygulanmıştır. Test görüntüleri üzerinde simülasyon gerçekleştirdikten sonra, geliştirilen sistemin F1-skor performansı yaklaşık %70 tanıma oranı olarak elde edilmiştir. Eğitilen Faster R-CNN modeli, uçak, otobüs durağı, stadyum ve güvenliğin önemli bir faktör olduğu kamu taşıtları da dâhil olmak üzere farklı halka açık yerler için kullanılabilir. Ayrıca, geliştirilen yöntem, tehlikeli nesnelerin raporlanması ve bu tür nesnelerin neden olduğu risklerin en aza indirilmesi açısından yerel gözetim sistemine gömülebilir.
Anahtar Kelimeler
Sİlah Tanıma Bıcak Tanıma Hızlı-Bölgesel Tabanlı Evrişimsel Sinir Ağı Yerel Gözetim Sistemi
Kaynakça
- Buckchash H. and Raman B., 2017, A robust object detector: application to detection of visual knives, International Conference on Multimedia & Expo Workshops, IEEE2017, 633-638.
- Girshick R., Donahue J., Darrell T. and Malik J., 2014. Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 580-587.
- Matiolański A., Maksimova A. and Dziech A., 2016. CCTV object detection with fuzzy classification and image enhancement, Multimedia Tools Applications, 75, 10513-10528.
- Ren S., He K., Girshick R. and Sun J., 2015. Faster R-CNN: Towards real-time object detection with region proposal networks, Advances in Neural Information Processing Systems, 91-99.
- Tiwari R.K. and Verma G.K., 2015. A computer vision based framework for visual gun detection using harris interest point detector, Procedia Computer Science, 54, 703-712.
- Yuenyong S., Hnoohom N. and Wongpatikaseree K., 2018. Automatic detection of knives in infrared images, Int. ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, IEEE2018, 65-68.
- 1- bbc.com, https://www.bbc.com/turkce/haberler-dunya-45195900, (05-06-2021).
- 2- everytownresearch.org, https://everytownresearch. org/gun-violence-america/, (05-06-2021).
- 3- Hurriyet.com, http://www.hurriyet.com.tr/kelebek/ saglik/her-yil-3-bin-kisi-silahla-oluyor-27289543, (05-06-2021).
- 4- labelimg, https://github.com/tzutalin/labelImg, (05-06-2021).
- 5- nytimes.com, https://www.nytimes.com/2018/12/18 /us/gun-deaths.html, (05-06-2021).
- 6- weapons-dataset, https://sci2s.ugr.es/weapons-detection, (05-06-2021).
- 7. youtube.com, https://www.youtube.com/watch?v= qV6hy28C8-8, (05-06-2021).
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Haziran 2021 |
| Gönderilme Tarihi | 11 Kasım 2020 |
| Yayımlandığı Sayı | Yıl 2021 Cilt: 21 Sayı: 3 |
