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Research Advanced in Image Recognition under Autonomous Driving Scene Based on Deep Learning
- 1 Ira A. Fulton Schools of Engineering, Arizona State University, AZ, USA
* Author to whom correspondence should be addressed.
Abstract
Image recognition has always been a fundamental research task in the computer vision community, aimed at identifying the categories of objects in images and has been widely used in many fields, especially in autonomous driving. Early image recognition technologies were mostly based on machine learning, and their recognition speed and accuracy could not meet the application requirements of complex autonomous driving scenarios. With the great success of convolutional neural networks, image recognition technology based on deep learning has attracted increasing research interest. Taking the autonomous driving scenario as an example, this article introduces the latest research progress of image recognition technology, including representative methods and their basic pipelines. In addition, this paper also introduces the commonly used dataset in image recognition and discusses the existing problems of image recognition in autonomous driving tasks. Finally, this paper looks forward to the future development directions of this field, hoping bring some new insight to advance the further development of image recognition under autonomous driving scene.
Keywords
Image recognition, autonomous driving, deep learning
[1]. Fujiyoshi, H., Hirakawa, T., & Yamashita, T. (2019). Deep learning-based image recognition for autonomous driving. IATSS Research, 43(4), 244-252.
[2]. Zhao, J., Zhao, W., Deng, B., Wang, Z., Zhang, F., Zheng, W., Cao, W., Nan, J., Lian, Y., & Burke, A. F. (2024). Autonomous driving system: A comprehensive survey. Expert Systems with Applications, 242, 122836.1-122836.27.
[3]. Lai, Y. (2019). A Comparison of Traditional Machine Learning and Deep Learning in Image Recognition. Journal of Physics. Conference Series, 1314(1), 12148.
[4]. Yu, W., & Ren, P. (2021). Vehicle and pedestrian target detection in auto driving scene. Journal of Physics: Conference Series, 2132(1), 12013.
[5]. Padmavathi, B., Dhivya, S., Datchanamoorthy, K., Banu, A. K., & Karthikeyan, S. M. (2024). Lane and Traffic Sign Detection in Self-Driving Cars using Deep Learning. International Journal of Vehicle Structures and Systems, 16(1), 45-49.
[6]. Geiger, A., Lenz, P., & Urtasun, R. (2012). Are we ready for autonomous driving? The KITTI vision benchmark suite. 2012 IEEE Conference on Computer Vision and Pattern Recognition, 3354-3361.
[7]. Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., & Schiele, B. (2016). The Cityscapes Dataset for Semantic Urban Scene Understanding. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3213–3223.
[8]. Sun, P. , Kretzschmar, H. , Dotiwalla, X. , Chouard, A. , Patnaik, V. , & Tsui, P. , et al. (2020). Scalability in Perception for Autonomous Driving: Waymo Open Dataset. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2443-2451.
[9]. Zhang, Y., Carballo, A., Yang, H., & Takeda, K. (2023). Perception and sensing for autonomous vehicles under adverse weather conditions: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 196, 146-177.
[10]. Hataba, M., Sherif, A., Mahmoud, M., Abdallah, M., & Alasmary, W. (2022). Security and Privacy Issues in Autonomous Vehicles: A Layer-Based Survey. IEEE Open Journal of the Communications Society, 3, 811–829.
Cite this article
Zhu,Z. (2025). Research Advanced in Image Recognition under Autonomous Driving Scene Based on Deep Learning. Theoretical and Natural Science,86,53-59.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Computing Innovation and Applied Physics
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