Transfer Learning for Object Classification in Video Frames: An Analysis of DenseNets

Transfer Learning for Object Classification in Video Frames: An Analysis of DenseNets
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© 2024 by IJETT Journal
Volume-72 Issue-10
Year of Publication : 2024
Author : Sara Bouraya, Abdessamad Belangour
DOI : 10.14445/22315381/IJETT-V72I10P105

How to Cite?
Sara Bouraya, Abdessamad Belangour, "Transfer Learning for Object Classification in Video Frames: An Analysis of DenseNets," International Journal of Engineering Trends and Technology, vol. 72, no. 10, pp. 46-54, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I10P105

Abstract
This work investigates the enhanced capabilities of transfer learning in computer vision, specifically focusing on object detection. We employ Dense Convolutional Network (DenseNet) architectures, renowned for their efficacy in extracting and reusing features for the purpose of object identification in video frames. These advanced networks can circumvent the generally substantial requirements for computational resources and time-consuming training associated with deep learning tasks by utilizing transfer learning techniques. Our research aims to optimize the performance of DenseNet121, DenseNet169, and DenseNet201 models by making minor adjustments using a meticulously selected dataset. The objective is to assess their efficacy in identifying and categorizing items. The results indicate that DenseNet121 and DenseNet201 attain remarkable validation accuracies of 0.9605, while DenseNet169 closely matches with an accuracy of 0.9585. Results showcase the versatility of DenseNet models in tackling various object detection tasks and their comparable performance levels. This means that any of the three models might work for real-world picture recognition tasks. Moreover, our discoveries establish a basis for future investigations into enhancing the reliability of models for real-world use in monitoring and autonomous car systems, capitalizing on the models' proven accuracy.

Keywords
CNN, Transfer learning, Object detection, DenseNet, Car object detection.

References
[1] Prince Kumar et al., “A Comparative Study of Object Detection Algorithms in A Scene,” International Journal of Engineering Research & Technology, vol. 8, no. 5, pp. 438-440, 2019.
[Google Scholar] [Publisher Link]
[2] Kai Kang et al., “Object Detection from Video Tubelets with Convolutional Neural Networks,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 817-825, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Bilel Benjdira et al., “Car Detection Using Unmanned Aerial Vehicles: Comparison between Faster R-CNN and YOLOv3,” 2019 1st International Conference on Unmanned Vehicle Systems-Oman, Muscat, Oman, pp. 1-6, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Nikhil Yadav, and Utkarsh Binay, “Comparative Study of Object Detection Algorithms,” International Research Journal of Engineering and Technology, vol. 4, no. 11, pp. 586-591, 2017.
[Google Scholar] [Publisher Link]
[5] Joseph Redmon et al., “You Only Look Once: Unified, Real-Time Object Detection,” 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Joseph Redmon, and Ali Farhadi, “YOLO9000: Better, Faster, Stronger,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263-7271, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Xiang Long et al., “PP-YOLO: An Effective and Efficient Implementation of Object Detector,” Arxiv, pp. 1-8, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Shaoqing Ren et al., “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137-1149, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Xin Lu et al., “Grid R-CNN,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7363-7372, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Ross Girshick, “Fast R-CNN,” 2015 IEEE International Conference on Computer Vision, Santiago, Chile, pp. 1440-1448, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” Advances in Neural Information Processing Systems, vol. 25, 2012.
[Google Scholar] [Publisher Link]
[12] Karen Simonyan, and Andrew Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Arxiv, pp. 1-14, 2014.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Kaiming He et al., “Deep Residual Learning for Image Recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 770-778, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Christian Szegedy et al., “Going Deeper with Convolutions,” 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, pp. 1-9, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Gao Huang et al., “Densely Connected Convolutional Networks,” 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 2261-2269, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Sinno Jialin Pan, and Qiang Yang, “A Survey on Transfer Learning,” IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345-1359, 2010.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Asmaul Hosna et al., “Transfer Learning: A Friendly Introduction,” Journal of Big Data, vol. 9, pp. 1-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Barret Zoph et al., “Learning Transferable Architectures for Scalable Image Recognition,” 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8697-8710, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Hana’ Abd Razak et al., “Anomalous Behaviour Detection Using Transfer Learning Algorithm of Series and DAG Network,” 2019 IEEE 9th International Conference on System Engineering and Technology, Shah Alam, Malaysia, pp. 505-509, 2019.
[CrossRef] [Google Scholar] [Publisher Link]