Comparative analysis on YOLO object detection with OpenCV

Document Type: Review Paper

Authors

Department of Computer Application, Jain (Deemed to-be) University, Bengaluru, Karnataka, India.

Abstract

Computer Vision is a field of study that helps to develop techniques to identify images and displays. It has various features like image recognition, object detection and image creation, etc. Object detection is used for face detection, vehicle detection, web images, and safety systems. Its algorithms are Region-based Convolutional Neural Networks (RCNN), Faster-RCNN and You Only Look Once Method (YOLO) that have shown state-of-the-art performance. Of these, YOLO is better in speed compared to accuracy. It has efficient object detection without compromising on performance.

Keywords

Main Subjects


[1]      Mohapatra, Hitesh. (2015). HCR (English) using neural network. International journal of advance research and innovative ideas in education, 1(4), 379385.

[2]      Mohapatra, H., & Rath, A. K. (2019). Detection and avoidance of water loss through municipality taps in India by using smart taps and ICT. IET Wireless sensor systems9(6), 447-457.

[3]      Mohapatra, H., & Rath, A. K. (2019). Fault tolerance in WSN through PE-LEACH protocol. IET wireless sensor systems9(6), 358-365.

[4]      Mohapatra, H., Debnath, S., & Rath, A. K. (2019). Energy management in wireless sensor network through EB-LEACH (No. 1192). Easy Chair.

[5]      Nirgude, V., Mahapatra, H., & Shivarkar, S. (2017). Face recognition system using principal component analysis & linear discriminant analysis method simultaneously with 3d morphable model and neural network BPNN method. Global journal of advanced engineering technologies and sciences, 4(1), 1-6.

[6]      Panda, M., Pradhan, P., Mohapatra, H., & Barpanda, N. K. (2019). Fault tolerant routing in heterogeneous environment. International journal of scientific & technology research, 8(8), 1009-1013.

[7]      Mohapatra, H., & Rath, A. K. (2019). Fault-tolerant mechanism for wireless sensor network. IET wireless sensor systems, 10(1), 23-30.

[8]      Swain, D., Ramkrishna, G., Mahapatra, H., Patr, P., & Dhandrao, P. M. (2013). A novel sorting technique to sort elements in ascending order. International journal of engineering and advanced technology3(1), 212-126.

[9]      Broumi, S., Dey, A., Talea, M., Bakali, A., Smarandache, F., Nagarajan, D., ... & Kumar, R. (2019). Shortest path problem using Bellman algorithm under neutrosophic environment. Complex & intelligent systems5(4), 409-416.

[10]   Kumar, R., Edalatpanah, S. A., Jha, S., Broumi, S., Singh, R., & Dey, A. (2019). A multi objective programming approach to solve integer valued neutrosophic shortest path problems. Neutrosophic sets and systems24, 134-149.

[11]   Kumar, R., Dey, A., Broumi, S., & Smarandache, F. (2020). A study of neutrosophic shortest path problem. In Neutrosophic graph theory and algorithms (pp. 148-179). IGI Global.

[12]   Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A novel approach to solve gaussian valued neutrosophic shortest path problems. Infinite study.

[13]   Kumar, R., Edalatpanah, S. A., Jha, S., Gayen, S., & Singh, R. (2019). Shortest path problems using fuzzy weighted arc length. International journal of innovative technology and exploring engineering8, 724-731.

[14]   Kumar, R., Edaltpanah, S. A., Jha, S., & Broumi, S. (2018). Neutrosophic shortest path problem. Neutrosophic sets and systems23(1), 2.

[15]   Kumar, R., Jha, S., & Singh, R. (2020). A different approach for solving the shortest path problem under mixed fuzzy environment. International journal of fuzzy system applications (IJFSA)9(2), 132-161.

[16]   Kumar, R., Jha, S., & Singh, R. (2017). Shortest path problem in network with type-2 triangular fuzzy arc length. Journal of applied research on industrial engineering4(1), 1-7.

[17]   Kumar, R., Edalatpanah, S. A., Jha, S., & Singh, R. (2019). A pythagorean fuzzy approach to the transportation problem. Complex & intelligent systems5(2), 255-263.

[18]   Smarandache, F., & Broumi, S. (Eds.). (2019). Neutrosophic graph theory and algorithms. Engineering science reference.

[19]   Singh, R., & Saxena, V. (2017). A new data transfer approach through fuzzy Vogel’s approximation method. International journal of advanced research in computer science8(3), 515-519.

[20]   Mohapatra, H., Panda, S., Rath, A., Edalatpanah, S., & Kumar, R. (2020). A tutorial on powershell pipeline and its loopholes. International journal of emerging trends in engineering research8(4).

[21]   Gayen, S., Smarandache, F., Jha, S., & Kumar, R. (2020). Interval-valued neutrosophic subgroup based on interval-valued triple T-Norm. In Neutrosophic sets in decision analysis and operations research (pp. 215-243). IGI Global.

[22]   Gayen, S., Smarandache, F., Jha, S., Singh, M. K., Broumi, S., & Kumar, R. (2020). Introduction to plithogenic subgroup. In Neutrosophic graph theory and algorithms (pp. 213-259). IGI Global.

[23]   Gayen, S., Jha, S., Singh, M., & Kumar, R. (2019). On a generalized notion of anti-fuzzy subgroup and some characterizations. International journal of engineering and advanced technology.

[24]   Xu, X. (2012). From cloud computing to cloud manufacturing. Robotics and computer-integrated manufacturing28(1), 75-86.

[25]   Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. California management review61(4), 5-14.

[26]   Sakhnini, J., Karimipour, H., Dehghantanha, A., Parizi, R. M., & Srivastava, G. (2019). Security aspects of Internet of Things aided smart grids: a bibliometric survey. Internet of things, 100111.

[27]   Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587). http://openaccess.thecvf.com/menu.py

[28]   Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: towards real-time object detection with region proposal networks. Advances in neural information processing systems (pp. 91-99). Neural Information Processing Systems Foundation, Inc.

[29]   Roh, M. C., & Lee, J. Y. (2017, May). Refining faster-RCNN for accurate object detection. 2017 fifteenth IAPR international conference on machine vision applications (MVA) (pp. 514-517). IEEE.

[30]   Zeng, X., Ouyang, W., Yang, B., Yan, J., & Wang, X. (2016, October). Gated bi-directional CNN for object detection. European conference on computer vision (pp. 354-369). Cham: Springer.

[31]   Erhan, D., Szegedy, C., Toshev, A., & Anguelov, D. (2014). Scalable object detection using deep neural networks. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2147-2154). http://openaccess.thecvf.com/menu.py

[32]   Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788). http://openaccess.thecvf.com/menu.py

[33]   Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016, October). Ssd: Single shot multibox detector. European conference on computer vision (pp. 21-37). Cham: Springer.

[34]   Viola, P., & Jones, M. (2001, December). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001 (Vol. 1, pp. I-I). IEEE.

[35]   Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems (pp. 1097-1105). Neural Information Processing Systems Foundation, Inc.

[36]   Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587). http://openaccess.thecvf.com/menu.py

[37]   Uijlings, J. R., Van De Sande, K. E., Gevers, T., & Smeulders, A. W. (2013). Selective search for object recognition. International journal of computer vision104(2), 154-171.

[38]   Girshick, R. (2015). Fast R-CNN. Proceedings of the IEEE international conference on computer vision (pp. 1440-1448). http://openaccess.thecvf.com/menu.py

[39]   Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems (pp. 91-99). Neural Information Processing Systems Foundation, Inc.

[40]   Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems (pp. 91-99). Neural Information Processing Systems Foundation, Inc.

[41]   Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: unified, real-time object detection. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 779-788). http://openaccess.thecvf.com/menu.py

[42]   Redmon, J., & Farhadi, A. (2017). YOLO9000: better, faster, stronger. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7263-7271). http://openaccess.thecvf.com/menu.py

[43]   Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., ... & Zitnick, C. L. (2014, September). Microsoft coco: Common objects in context. European conference on computer vision (pp. 740-755). Cham: Springer.

[44]   Viola, P., & Jones, M. (2001, December). Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition (CVPR) (Vol. 1, pp. I-I). IEEE.

[45]   Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems (pp. 1097-1105). Neural Information Processing Systems Foundation, Inc.

[46]   Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. Computer vision and pattern recognition. https://arxiv.org/abs/1409.1556

[47]   Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9). http://openaccess.thecvf.com/menu.py

[48]   Pobar, M., & Ivasic-Kos, M. (2017, August). Multi-label poster classification into genres using different problem transformation methods. International conference on computer analysis of images and patterns (pp. 367-378). Cham: Springer.

[49]   Bishop, G., & Welch, G. (2001). An introduction to the kalman filter. Proc of SIGGRAPH, Course8(27599-23175), 41. Los Angeles: SIGGRAPH.

[50]   Horprasert, T., Harwood, D., & Davis, L. S. (1999, September). A statistical approach for real-time robust background subtraction and shadow detection. In IEEE ICCV (pp. 1-19). Citeseer.

[51]   Lipton, A. J., Fujiyoshi, H., & Patil, R. S. (1998, October). Moving target classification and tracking from real-time video. Proceedings fourth IEEE workshop on applications of computer vision. WACV'98 (Cat. No. 98EX201) (pp. 8-14). IEEE.

[52]   Stauffer, C., & Grimson, W. E. L. (1999, June). Adaptive background mixture models for real-time tracking. Proceedings. 1999 IEEE computer society conference on computer vision and pattern recognition (Cat. No PR00149) (Vol. 2, pp. 246-252). IEEE.

[53]   Liu, Y., Ai, H., & Xu, G. Y. (2001, September). Moving object detection and tracking based on background subtraction. Proc. SPIE 4554, object detection, classification, and tracking technologies (Vol. 4554, pp. 62-66). https://doi.org/10.1117/12.441618

[54]   Desa, S. M., & Salih, Q. A. (2004, July). Image subtraction for real time moving object extraction. Proceedings. International conference on computer graphics, imaging and visualization, 2004. CGIV 2004. (pp. 41-45). Penang, Malaysia, Malaysia: IEEE.

[55]   Sungandi, B., Kim, H., Tan, J. K., & Ishikawa, S. (2009). Real time tracking and identification of moving persons by using a camera in outdoor environment. International journal of innovative computing, information and control, 5, 1179-1188.

[56]   Jacques, J. C. S., Jung, C. R., & Musse, S. R. (2005, October). Background subtraction and shadow detection in grayscale video sequences. XVIII Brazilian symposium on computer graphics and image processing (SIBGRAPI'05) (pp. 189-196). IEEE.

[57]   Satoh, Y., Kaneko, S. I., & Igarashi, S. (2004). Robust object detection and segmentation by peripheral increment sign correlation image. Systems and computers in Japan35(9), 70-80.

[58]   Beymer, D., & Konolige, K. (1999, September). Real-time tracking of multiple people using continuous detection. Paper presented at the IEEE frame rate workshop (pp. 1-8). Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.46.7987&rep=rep1&type=pdf

[59]   Rosales, R., & Sclaroff, S. (1999, June). 3D trajectory recovery for tracking multiple objects and trajectory guided recognition of actions. Proceedings. 1999 IEEE computer society conference on computer vision and pattern recognition (Cat. No PR00149) (Vol. 2, pp. 117-123). IEEE.

[60]   Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei-Fei, L. (2009, June). ImageNet: a large-scale hierarchical image database. 2009 IEEE conference on computer vision and pattern recognition (pp. 248-255). IEEE.

[61]   Anonymous. (2017). Nutrition & exercise - timing is everything. Retrieved October 12, 2019 from https://blog.nasm.org/workout-and-nutrition-timing

[62]   Rosebrock, A. (2018). YOLO object detection with OpenCV. Retrieved December 25, 2019 from https://www.pyimagesearch.com/2018/11/12/yolo-object-detection-with-opencv/

[63]   Kappan, R. (2019). Average commute speed in Bengaluru drops 18.7 kmph. Retrieved December 21, 2019 from https://www.deccanherald.com/city/life-in-bengaluru/average-commute-speed-in-bengaluru-drops-to-187-kmph-757943.html