VEHICLE DETECTION AND IDENTIFICATION WITH SMALL DATASET USING FEW-SHOT LEARNING
DOI:
https://doi.org/10.29284/ijasis.9.2.2023.55-66Keywords:
Few-Shot object detection, few-shot learning, vehicle identification, vehicle classification.Abstract
Vehicle detection and identification serve an important role in employing autonomous vehicle classification. However, most deep learning methods for vehicle detection rely on large number of datasets for the training to perform well. The dataset shortage has become a main problem to acquiring a high accuracy detection. A detection method that could adapt with small samples could be a powerful solution for this problem. One of the popular methods is the Few-Shot Learning (FSL) algorithm. This method utilizes meta learning that can deliver more accurate detection with a small number of datasets. This research aimed to assess the effectiveness of the FSL algorithm in classifying road vehicles in Indonesia. Road vehicles categorization which based on the Decree of the Indonesian Minister of Public Works is unique, whereby it differentiates the trucks based on their axles. The datasets for these trucks are rarely available. This research work has employed a small amount of data to the FSL algorithm with reweighting module (FSRW) for classification. The results have demonstrated that FSRW method with fine tuning achieved better results in road vehicle identification with around 33% increase compared to the baseline YOLO method in categorizing road vehicles in Indonesia context.
References
G.S. Dhillon, P. Chaudhari, A. Ravichandran and S. Soatto, “A baseline for few-shot image classification,” International Conference on Learning Representations, 2019, pp. 1–20.
Z. Moutakki, I.M. Ouloul, K. Afdel and A. Amghar, “Real-time system based on feature extraction for vehicle detection and classification,” Transport Telecommunication, vol. 19, no. 2, 2018, pp. 93–102.
K. Balaji, M. Rabiei, V. Suicmez, C.H. Canbaz, Z. Agharzeyva, S. Tek and C. Temizel, "Status of data-driven methods and their applications in oil and gas industry" 80th EAGE Conference and Exhibition, 2018, pp. 1-15.
W.Q. Zhang, “Numerical simulation in forward hot-extrusion and back hot-extrusion forming process for micro-gear,” DEStech Transactions on Engineering and Technology Research, 2017, pp.1-12.
R. Berwick, “An idiot’s guide to support vector machines (SVMs): A new generation of learning algorithms key ideas,” Massachusetts Institute of Technology, 2003, pp. 1–28.
H.K. Leung, X.Z. Chen, C.W. Yu, H.Y. Liang, J.Y. Wu and Y.L. Chen, “A deep-learning-based vehicle detection approach for insufficient and nighttime illumination conditions,” Applied Sciences, vol. 9, no. 22, 2019, pp. 1-27.
B. Mahaur, N. Singh and K.K. Mishra, “Road object detection: A comparative study of deep learning-based algorithms,” Multimedia Tools and Applications, vol. 81, no. 10, 2022, pp. 14247–14282.
A.R. Pathak, M. Pandey and S. Rautaray, “Application of deep learning for object detection,” Procedia Computer Sciences, vol. 132, 2018, pp. 1706–1717.
A. Majee, K. Agrawal and A. Subramanian, “Few-shot learning for road object detection,” 2021, pp. 1-8 [Online]. Available: http://arxiv.org/ abs/ 2101.12543
S. Gidaris, P. Paristech and N. Komodakis, “Dynamic few-shot visual learning without forgetting : Supplementary material,” Conference on Computer Vision and Pattern Recognition, vol. 9, 2018, pp. 4367–4375.
G. Chandan, A. Jain, H. Jain, and Mohana, “Real time object detection and tracking using deep learning and OpenCV,” International Conference on Inventive Research in Computing Applications, 2018, pp. 1305–1308.
Y. Fan, Y. Li and A. Zhu, “A few-shot learning algorithm based on attention adaptive mechanism,” Journal of Physics: Conference Series, vol. 1966, no. 1, 2021, pp. 1-7.
R. Zhang, T. Che, Y. Bengio, Z. Ghahramani and Y. Song, “Metagan: An adversarial approach to few-shot learning,” Advances in Neural Information Processing Systems, 2018, pp. 2365–2374.
Z. Zhou, S. Li, W. Guo and Y. Gu, “Few-shot aircraft detection in satellite videos based on feature scale selection pyramid and proposal contrastive learning,” Remote Sensing, vol. 14, no. 18, 2022, pp. 1-19.
Y. Wang, Q. Yao, J.T. Kwok and L.M. Ni, “Generalizing from a few examples: A survey on few-shot learning,” ACM Computing Surveys, vol. 53, no. 3, 2020, pp. 1–34.
W. Wu, H. Liu, L. Li, Y. Long, X. Wang, Z. Wang and Y. Chang, “Application of local fully convolutional neural network combined with YOLO v5 algorithm in small target detection of remote sensing image,” PLoS One, vol. 16, no. 10, 2021, pp. 1–15.
B. Kang, Z. Liu, X. Wang, F. Yu, J. Feng and T. Darrell, “Few-shot object detection via feature reweighting,” International Conference on Computer Vision, 2019, pp. 8419–8428.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 SettingsMuzakki Afandi, SettingsMedia Anugerah Ayu
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
