An Autonomous Driving Based on Deep Learning Image Recognition

Main Article Content

Article Sidebar

Published Jul 31, 2021
Muhammad Afzal Nazim

Abstract

Image recognition refers, in the context of machine vision, the software's ability to recognize images in objects, places, people, writing and activities. Computers can use camera vision technology and artificial intelligence software to recognize pictures. The purpose of this research is to provide the latest technology technique for images recognition for driver. During the driving they can use this technology to detect deferent object. In the years before 2010, researchers used the local picture characteristics that they had identified using hybrid machine learning methods to address problems relating to image identification and classification. Since the year 2010, however, several deep learning techniques for image recognition have been developed and put through their paces. When it comes to general object identification contests, techniques that employ deep learning to identify images surpass tactics that were utilized before deep learning was introduced by a significant margin. In addition to explaining how deep learning is used to address problems in photo identification, this article covers the latest deep learning imaging technologies available at the time of writing (at the time of writing). It is hard and time-consuming to detect the appropriate mapping function from many training data and instructor labels in a time-consuming and difficult field of image recognition technology. This article discusses how deep learning is being used in the area of image identification, as well as the most recent advancements in autonomous deep learning driving, as a result of these advances. The research aim is to provide the latest technology  with the use of deep learning for images recognition.

 

How to Cite

Nazim, M. A. . (2021). An Autonomous Driving Based on Deep Learning Image Recognition. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/86
Abstract 35 | PDF Version Download Downloads 6

Article Details

References
References
1) Histograms of oriented gradients, N. Dalal, B. Triggs,https://doi.org/10.1109/CVPR.2005.177.
2) Distinctive image features from scale-invariant keypoints, D.G. Lowe, Int. J. Compute. Vis. 60 (2004) 91–110.
3) Visual explanation by attention branch network , K. Mori, H. Fukui, T. Murase, T. Hirakawa, T. Yamashita, H. Fujiyoshi, Proc. of IEEE Intelligent Vehicles Symposium (9-12 June 2019) https://doi.org/10.1109/IVS.2019.8813900.
4) Learning deep features for discriminative localization, by B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, A. Torralba,
5) Realtovirtual domain unification for end-to- end autonomous driving, Y. Luona, L. Xiaodan, W. Tairui, X. Eric, Proc. of European Conference on Computer Vision (2018) 553–570.
6) Attention neural baby talk by Y. Mori, H. Fukui, T. Hirakawa, N. Jo, T. Yamashita, H. Fujiyoshi.
7) The cityscapes dataset for semantic urban scene understanding, M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, B. Schiele, Proc. of IEEE Conference on Computer Vision and Pattern Recognition (27-30 June 2016) https://doi.org/10.1109/CVPR.2016.350.
8) Hui Luan, Peter Geczy, Hollis Lai, Janice Gobert, Challenges and Future Directions of Big Data and Artificial Intelligence in Education, https://doi.org/10.3389/fpsyg.2020.580820
9) Muhammad Alam, Jian-Feng Wang, Cong Guangpei, LV Yunrong , Yuanfang Chen , Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images,https://link.springer.com/article/10.1007/s11036-020-01703-3
10) Richmond Alake, How Does AI Detect Objects,https://towardsdatascience.com/how-does-ai-detect-objects-technical-d8d63fc12881
11) Jing Ren, Xishi Huang, From traditional to deep learning, https://doi.org/10.1016/B978-0-12-822314-7.00013-4
12) Rapid object detection using a boosted cascade of simple features, P. Viola, M. Jones, Proc. of IEEE Conference on Computer Vision and Pattern Recognition, https://doi.org/10.1109/CVPR.2001.990517.
Section
GE3- Computers & Information Technology