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- Vol. 5 Issue 5
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Crime and Robbery Detection
Authors
1Student, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Assistant professor, MCA, Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Article Information
- Published In
- Volume 5, Issue 5 (2025)
- Published Online
- September-October 2025
- Pages
- 61-66
- Article Type
- Original Article
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References
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2. J. Redmon and A. Farhadi, "YOLOv3: An Incremental Improvement," arXiv preprint arXiv:1804.02767, 2018.
3. C. Y. Wang, A. Bochkovskiy, and H. Y. M. Liao, "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors," arXiv preprint arXiv:2207.02696, 2022.
4. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, 2018, pp. 4510–4520.
5. P. Viola and M. J. Jones, "Robust Real-Time Face Detection," International Journal of Computer Vision, vol. 57, no. 2, pp. 137–154, 2004.
6. M. S. Reiss, R. Spivak, and H. N. Nguyen, "Smart Surveillance System Using Deep Learning and IoT," in Proc. IEEE Intl. Conf. on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2020, pp. 1–6.
7. K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," arXiv preprint arXiv:1409.1556, 2014.
8. S. Sultana, A. Sufian, and P. Dutta, "A Review of Object Detection Models Based on Convolutional Neural Network," in Intelligent Computing: Image Processing Based Applications, Springer, 2020, pp. 1–16.
9. R. Singh and V. Arora, "Real-Time Weapon Detection Using Convolutional Neural Networks," in Proc. IEEE Intl. Conf. on Information and Communication Technology (CICT), 2019, pp. 1–6.
10. N. Bhattacharya and A. Basu, "A Deep Learning Approach for Smart Surveillance Using YOLO and LSTM," in Proc. IEEE Intl. Conf. on Advances in Computing, Communications and Informatics (ICACCI), 2021, pp. 1022–1026.
2. J. Redmon and A. Farhadi, "YOLOv3: An Incremental Improvement," arXiv preprint arXiv:1804.02767, 2018.
3. C. Y. Wang, A. Bochkovskiy, and H. Y. M. Liao, "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors," arXiv preprint arXiv:2207.02696, 2022.
4. M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA, 2018, pp. 4510–4520.
5. P. Viola and M. J. Jones, "Robust Real-Time Face Detection," International Journal of Computer Vision, vol. 57, no. 2, pp. 137–154, 2004.
6. M. S. Reiss, R. Spivak, and H. N. Nguyen, "Smart Surveillance System Using Deep Learning and IoT," in Proc. IEEE Intl. Conf. on Computer Communication and Informatics (ICCCI), Coimbatore, India, 2020, pp. 1–6.
7. K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," arXiv preprint arXiv:1409.1556, 2014.
8. S. Sultana, A. Sufian, and P. Dutta, "A Review of Object Detection Models Based on Convolutional Neural Network," in Intelligent Computing: Image Processing Based Applications, Springer, 2020, pp. 1–16.
9. R. Singh and V. Arora, "Real-Time Weapon Detection Using Convolutional Neural Networks," in Proc. IEEE Intl. Conf. on Information and Communication Technology (CICT), 2019, pp. 1–6.
10. N. Bhattacharya and A. Basu, "A Deep Learning Approach for Smart Surveillance Using YOLO and LSTM," in Proc. IEEE Intl. Conf. on Advances in Computing, Communications and Informatics (ICACCI), 2021, pp. 1022–1026.
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