[1] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-based convolutional networks for accurate object detection and segmentation,” IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 1, pp. 142-158, 2015.
[2] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788, 2016.
[3] W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in European conference on computer vision, pp. 21-37, Springer, 2016.
[4] S. Ren, K. He, R. B. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1137-1149, 2017.
[5] Z. Cai and N. Vasconcelos, “Cascade r-cnn: Delving into high quality object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 6154-6162, 2018.
[6] J. Redmon and A. Farhadi, “Yolov3: An incremental improvement,” arXiv preprint arXiv:1804.02767, 2018.
[7] A. Bochkovskiy, C.-Y. Wang, and H.-Y. M. Liao, “Yolov4: Optimal speed and accuracy of object detection,” arXiv preprint arXiv:2004.10934, 2020.
[8] G. Jocher, “ultralytics/yolov5: v3.1 - bug ixes and performance improvements.” https://github.com/ultralytics/yolov5, 2020.
[9] P. Adarsh, P. Rathi, and M. Kumar, “Yolo v3-tiny: Object detection and recognition using one stage improved model,” in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS), pp. 687-694, 2020.
[10] P. Cernek, N. Bollig, K. Anklam, and D. Dopfer, “Hot topic: Detecting digital dermatitis with computer vision,” Journal of Dairy Science, vol. 103, no. 10, pp. 9110-9115, 2020.