An Efficient Security System That Uses Artificial Intelligence to Detect and Identify Objects
Object identification is a significant task in computer vision due to the complexity and diversity of the things that must be detected. Rapid response time and precision are critical, particularly in security applications. We investigate YOLOv5, one of the most efficient object identification algorithms on the market, in this study. Our goal is to show how successful this algorithm is in a security system when compared to other existing alternatives. We also created a web interface that allows visitors to view the live camera feed and track the object detection process in real time. We provide our action plan, as well as the technology and knowledge required to complete this project. The suggested security system consists of a high-resolution surveillance camera and the YOLOv5 object detection algorithm. We created and implemented this system using computer programming and image processing technologies. Our findings reveal that the YOLOv5 algorithm outperforms alternative solutions in terms of speed and accuracy.
Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv preprint arXiv:2004.10934.
Liew, J. W. M., & Law, N. F. (2018). A review on video-based human activity recognition. arXiv preprint arXiv:1807.06306.
Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767.
Saha, S., & Chowdhury, A. S. (2020). Artificial intelligence-based smart security and surveillance system: a review. Multimedia Systems, 26(2), 149-164.
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). Springer, Cham.
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).
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., ... & Berg, A. C. (2015). ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211-252.
Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning (pp. 6105-6114).