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Object Identification: A Review

Abhijeet Tripathi, Anurag Shekhar, Krishna Manglam, P. K. Mishra

Abstract


Artificial neural networks are the best and most popular method for classifying images and identifying objects in images. The paper examines them as a technique that greatly enhances the aforementioned, extremely challenging computer calculations later section of the publication includes a picture of the chosen object detector we used for our introduction experiment as well as a brief overview of its development. Also presented is a fresh way for automatically producing brand-new domain-specific datasets, which are essential during the training stage of neural networks. This proposal for future study will be based on the experiment that was completed.


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References


Diwan, T., Anirudh, G., & Tembhurne, J. V. (2023). Object detection using YOLO: Challenges, architectural successors, datasets and applications. Multimedia Tools and Applications, 82(6), 9243-9275.

Ahmad, T., Ma, Y., Yahya, M., Ahmad, B., Nazir, S., & Haq, A. U. (2020). Object detection through modified YOLO neural network. Scientific Programming, 2020, 1-10.

Lee, Y. H., & Kim, Y. (2020). Comparison of CNN and YOLO for Object Detection. Journal of the semiconductor & display technology, 19(1), 85-92.

Liu, C., Tao, Y., Liang, J., Li, K., & Chen, Y. (2018, December). Object detection based on YOLO network. In 2018 IEEE 4th information technology and mechatronics engineering conference (ITOEC) (pp. 799-803). IEEE.

Long, X., Deng, K., Wang, G., Zhang, Y., Dang, Q., Gao, Y., ... & Wen, S. (2020). PP-YOLO: An effective and efficient implementation of object detector. arXiv preprint arXiv:2007.12099.

Huang, R., Pedoeem, J., & Chen, C. (2018, December). YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. In 2018 IEEE international conference on big data (big data) (pp. 2503-2510). IEEE.

Liu, W., Ren, G., Yu, R., Guo, S., Zhu, J., & Zhang, L. (2022, June). Image-adaptive YOLO for object detection in adverse weather conditions. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 2, pp. 1792-1800).

Huang, Z., Wang, J., Fu, X., Yu, T., Guo, Y., & Wang, R. (2020). DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection. Information Sciences, 522, 241-258.

Nguyen, D. T., Nguyen, T. N., Kim, H., & Lee, H. J. (2019). A high-throughput and power-efficient FPGA implementation of YOLO CNN for object detection. IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 27(8), 1861-1873.

Krišto, M., Ivasic-Kos, M., & Pobar, M. (2020). Thermal object detection in difficult weather conditions using YOLO. IEEE access, 8, 125459-125476.


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