DOI: 10.55524/ijircst.2022.10.3.55 | DOI URL: https://doi.org/10.55524/ijircst.2022.10.3.55
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
Article Tools: Print the Abstract | Indexing metadata | How to cite item | Email this article | Post a Comment
Dr. Ashish Oberoi
In the midst of the efforts in an item identification, region CNNs (rCNN) stands out as the most impressive, combining discriminatory exploration, CNNs, sustenance vector machines (SVM), and bounding box regression to achieve excellent object detection performance. We propose a new method for identifying numerous items from pictures using convolution neural nets (CNNs) in this presented study. The authors of the presented study use the edge box technique to create region suggestions from edge maps for each picture in our model, and then forward pass all of the proposals through a well-accepted CaffeNet prototype. Then we extract the yield of softmax that generally is most recent layer of CNN, to determine CNNs score for every proposal. One of the greedy suppression methodology referred to as non-maximum suppression (NMS) method is then used to combine the suggestions for each class separately. Finally, we assess each class's mean average precision (mAP). On the PASCAL 2007 test dataset, our model has a mAP of 37.38 percent. In this work, we also explore ways to enhance performance based on our model.
1. Shah M, Kapdi R. Object detection using deep neural networks. In: Proceedings of the 2017 International Conference on Intelligent Computing and Control Systems, ICICCS 2017. 2017.
2. Xiao F, Deng W, Peng L, Cao C, Hu K, Gao X. Multi-scale deep neural network for salient object detection. IET Image Process. 2018;
3. Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, et al. ImageNet Large Scale Visual Recognition Challenge. Int J Comput Vis. 2015;
4. Zhang X, Chen F, Huang R. A combination of RNN and CNN for attention-based relation classification. In: Procedia Computer Science. 2018.
5. Joseph S, Pradeep A. Object Tracking using HOG and SVM. Int J Eng Trends Technol. 2017;
6. Wang W, Zhu Y, Wang Z, Tu H. Intelligent robot object detection algorithm based on spatial pyramid and integrated features. Jisuanji Jicheng Zhizao Xitong/Computer Integr Manuf Syst CIMS. 2017;
7. Ren S, He K, Girshick R, Sun J. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans Pattern Anal Mach Intell. 2017;
8. Chen Y, Li W, Sakaridis C, Dai D, Van Gool L. Domain Adaptive Faster R-CNN for Object Detection in the Wild. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2018.
9. Guo MW, Zhao YZ, Xiang JP, Zhang C Bin, Chen ZH. Review of object detection methods based on SVM. Kongzhi yu Juece/Control and Decision. 2014.
10. Liu J, Huang Y, Peng J, Yao J, Wang L. Fast Object Detection at Constrained Energy. IEEE Trans Emerg Top Comput. 2018;
Assistant Professor, Department of Computer Science & Engineering, RIMT University, Mandi Gobindgarh, Punjab, India
No. of Downloads: 11 | No. of Views: 275
Dr. A. Seshagiri Rao, V. Nagarjuna, M. Sivudu.
November 2022 - Vol 10, Issue 6
Dr. Satish Saini.
March 2022 - Vol 10, Issue 2
March 2022 - Vol 10, Issue 2