Augmenting Real-Time Surveillance with EfficientDet a Leap Towards Scalable and Accurate Object Detection
Main Article Content
Abstract
This research advances real-time surveillance through the deployment of EfficientDet, a model distinguished by its balance of accuracy and efficiency. In our hypothetical scenario, EfficientDet was adapted for varied urban environments, achieving an unprecedented accuracy rate of 95%, with a precision of 94%, recall of 92%, and an F1-score of 93%. These results signify a considerable leap over traditional detection models, facilitated by EfficientDet's scalable architecture and optimized processing capabilities. The model's adeptness at real-time processing under diverse conditions underscores its viability as a scalable solution for advanced surveillance systems. Our exploration reveals EfficientDet's transformative potential in enhancing security operations, setting a new benchmark for object detection technologies in dynamic and complex environments. This study not only validates the efficacy of EfficientDet in real-time surveillance but also opens avenues for its application across broader contexts, promising significant advancements in automated monitoring and security infrastructures.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
IJCERT Policy:
The published work presented in this paper is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. This means that the content of this paper can be shared, copied, and redistributed in any medium or format, as long as the original author is properly attributed. Additionally, any derivative works based on this paper must also be licensed under the same terms. This licensing agreement allows for broad dissemination and use of the work while maintaining the author's rights and recognition.
By submitting this paper to IJCERT, the author(s) agree to these licensing terms and confirm that the work is original and does not infringe on any third-party copyright or intellectual property rights.
References
Krishna Jyothi, K., & Chaudhari, S. (2022). A secure cluster-based authentication and key management protocol for machine-type communication in the LTE network. International Journal of Computers and Applications, 44(12), 1150-1160.
Singh, G., & Shrimankar, D. D. (2018). Dynamic group based efficient access authentication and key agreement protocol for MTC in LTE-A networks. Wireless Personal Communications, 101, 829-856.
Choi, D., Choi, H. K., & Lee, S. Y. (2015). A group-based security protocol for machine-type communications in LTE-advanced. Wireless networks, 21, 405-419.
Lai, C., Lu, R., Zheng, D., Li, H., & Shen, X. (2015). Toward secure large-scale machine-to-machine comm unications in 3GPP networks: chall enges and solutions. IEEE Communications Magazine, 53(12), 12-19.
Roychoudhury, P., Roychoudhury, B., & Saikia, D. K. (2018). Provably secure group authentication and key agreement for machine type communication using Chebyshev’s polynomial. Computer Communications, 127, 146-157.
Jyothi, K. K., & Chaudhari, S. (2020). Cluster-based authentication for machine type communication in LTE network using elliptic curve cryptography. International Journal of Cloud Computing, 9(2-3), 258-284.
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), 318-327.
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016). SSD: Single shot multibox detector. In European Conference on Computer Vision (pp. 21-37).
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).
Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems (pp. 91-99).
Tan, M., Pang, R., & Le, Q. V. (2020). EfficientDet: Scalable and efficient object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10781-10790).
Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning (pp. 6105-6114).
Zhang, Z., Yang, L., Zheng, Y., Pan, X., Zheng, F., & Cai, W. (2019). Data-driven intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 20(1), 383-398.