Research Article
Year 2021,
Volume: 5 Issue: 1, 25 - 40, 30.06.2021
Abstract
According to the reports of Public Health Institution, approximately 250,000 rabies-risk animal bites occur per year in Turkey. Most of these bites are caused by dogs and most of the victims are the children who play in playgrounds. With the development of deep learning-based computer vision technology, autonomous detection of dangerous objects (handguns, knives, dogs, etc.) in these children’s playgrounds has become a crucial security application. In this paper, a real-time dog detection model based on YOLOv3 deep learning algorithm is proposed as a new smart city security application and this model is applied to the selected children’s playground. Firstly, in view of the problem of insufficient stray dog image data in the original datasets, new images of stray dogs have been taken from an animal shelter and they have been added to the dataset. These new images have effectively enriched the diversity of training data and improved the training performance of the proposed model. The proposed model has been optimized by utilizing various hyperparameters and the results have been compared with each other. The model with the best evaluation scores is proposed and applied to detect dogs automatically by the fast emergency station located in the selected children’s playground. The real-time application has achieved 82.59% of AP with adjusted hyperparameters.
Thanks
We would like to thank Bursa Metropolitan Municipality for providing the fast emergency stations and the other resources for conducting our experiments.
References
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Year 2021,
Volume: 5 Issue: 1, 25 - 40, 30.06.2021
Abstract
References
- https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS?contextual=default Accessed: 10 August 2020.
- https://www.bursa.bel.tr/cocuklara-buyuksehir-korumasi/haber/27347 Accessed: 10 August 2020.
- https://www.dogsbite.org/reports/14-year-dog-bite-fatality-chart-trends-in-3-periods-2005-2018.pdf Accessed: 08 October 2020.
- Aylan, O., Baykam, N., Güner, R., Kara, A., Köksal, İ., Seçer, M., Tülek, N., Ünal, N. (2019). T.C. Sağlık Bakanlığı Kuduz Profilaksi Rehberi.
- Aksoy, M., Demirbaş, B., Maden, F., Şimşek, Ç., Özlü, A., Kaya, M., Çulha, G., Topaç, O., Arı, E., Yılmaz, H., Gürbüz, F. (2010). Ankara İlinde 2005–2009 yılları arasında görülen şüpheli ısırıkların ve kuduz aşılamasının değerlendirilmesi, Türkiye 3. EKMUD Kongresi Özet Kitabı, p.199.
- Gunduz, T., Elcioglu, O. and Balci, Y. (2011). An Evaluation of Dog And Cat Bites For a Five Year Period: A Sample Case From Eskisehir, Turkish Journal of Trauma and Emergency Surgery, 17(2):133–140.
- Karadağ, M., Çatak, B., Baştürk, S. and Elmas, Ş. (2014). Assessment of Rabies-Risk Contact Notifications In Yıldırım, District of Bursa, Turkish Journal of Family Practice, 18(3): 117–121.
- Tekyol, D. (2019). Evaluation of rabies risky contact cases admitted to emergency department last year, Haydarpasa Numune Medical Journal, 2019.83723.
- İnci, A., Doğanay, M., Özdarendeli, A., Düzlü, Ö. and Yıldırım, A. (2018). Overview of Zoonotic Diseases in Turkey: The One Health Concept and Future Threats, Türkiye Parazitoloji Dergisi, 42(1):39–80.
- Krizhevsky, A., Sutskever, I. and Hinton, G. E. (2017). ImageNet Classification With Deep Convolutional Neural Networks, Commun. ACM, 60(6): 84–90.
- Luna, E., San Miguel, J., Ortego, D. and Martínez, J. (2018). Abandoned Object Detection in Video-Surveillance: Survey and Comparison, Sensors, 18(12), 4290.
- Namozov, A. and Cho, Y. I. (2018). An Efficient Deep Learning Algorithm for Fire and Smoke Detection with Limited Data, Advances in Electrical and Computer Engineering, 18: 121–128.
- Sun, Y., Liang, D., Wang, X. and Tang, X. (2015). DeepID3: Face Recognition with Very Deep Neural Networks, arXiv:1502.00873 .
- Wang, T., Miao, Z., Chen, Y., Zhou, Y., Shan, G. and Snoussi, H. (2019). AED-Net: An Abnormal Event Detection Network, Engineering, 5(5): 930–939.
- Lai, J. and Maples, S. (2017). Developing a Real-Time Gun Detection Classifier, World Academy of Science, Stanford University, p.4.
- Olmos, R., Tabik, S. and Herrera, F. (2017). Automatic Handgun Detection Alarm in Videos Using Deep Learning, Neurocomputing, 275: 66-72.
- Fang, W., Wang, L. and Ren, P. (2020). Tinier-YOLO: A Real-Time Object Detection Method for Constrained Environments, IEEE Access, pp. 1-1.
- Redmon, J. and Farhadi, A. (2018). YOLOv3: An Incremental Improvement, arXiv:1804.02767
- https://cocodataset.org/ Accessed: 30 November 2020.
- https://storage.googleapis.com/openimages/web/index.html/ Accessed: 30 November 2020.
- https://developer.nvidia.com/blog/deploying-a-scalable-object-detection-inference-pipeline/ Accessed 27 September 2020.
- Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 779–788
- https://github.com/tzutalin/labelImg/ Accessed: 30 November 2020.
There are 23 citations in total.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Research Articles |
| Authors | |
| Publication Date | June 30, 2021 |
| Published in Issue | Year 2021Volume: 5 Issue: 1 |
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