Perbandingan Efficientnet, Visual Geometry Group 16, dan Residual Network 50 Untuk Klasifikasi Kendaraan Bermotor


Keywords: Vehicle Classification; Convolutional Neural Network (CNN); EfficientNet; VGG16; ResNet50; Transfer Learning

Abstract

This study compares the performance of three Convolutional Neural Network (CNN) models—EfficientNet, VGG16, and ResNet50—in motor vehicle classification tasks using the "Car vs Bike" dataset. Transfer learning was applied using pretrained weights from ImageNet. The results indicate that VGG16 achieved the best performance with 95% accuracy, precision of 0.95, recall of 0.96, and an F1-score of 0.95, demonstrating high balance in recognizing both classes. ResNet50 attained 87% accuracy on the test dataset with a precision of 0.89, recall of 0.84, and an F1-score of 0.87, offering a trade-off between accuracy and computational efficiency. Conversely, EfficientNet exhibited the lowest performance with 50% accuracy, failing to recognize the "Car" class effectively, as evidenced by precision and recall values of 0.00. Factors such as architectural complexity, dataset bias, and computational efficiency influenced these outcomes. This study reinforces previous findings on the strengths and weaknesses of CNN models in motor vehicle classification applications. Furthermore, it highlights the importance of balanced data management and model selection tailored to specific application requirements. However, the dataset's limitation of only two classes and reliance on transfer learning remain areas for future improvement. These findings provide valuable insights for developing intelligent transportation systems requiring high accuracy and efficiency.

Downloads

Download data is not yet available.

References

Y. Bi, B. Xue, and M. Zhang, “Genetic Programming With a New Representation to Automatically Learn Features and Evolve Ensembles for Image Classification,” Ieee Trans. Cybern., vol. 51, no. 4, pp. 1769–1783, 2021, doi: 10.1109/tcyb.2020.2964566.

J. Moon and S. Park, “Robust Visual Detection of Brake-Lights in Front for Commercialized Dashboard Camera,” PLoS One, vol. 18, no. 8, p. e0289700, 2023, doi: 10.1371/journal.pone.0289700.

C. Shekhar and S. Saha, “Real Time Vehicle Identification,” 2022, doi: 10.48550/arxiv.2207.08081.

N. S. S. Pal, P. Raymahapatra, S. Paul, S. Dolui, A. K. Chaudhuri, and S. Das, “A Novel Brain Tumor Classification Model Using Machine Learning Techniques,” Int. J. Eng. Technol. Manag. Sci., vol. 7, no. 2, pp. 87–98, 2023, doi: 10.46647/ijetms.2023.v07i02.011.

A. Mirzazadeh, A. Azizi, Y. Abbaspour‐Gilandeh, J. L. Hernández-Hernández, M. Hernández-Hernández, and I. Gallardo-Bernal, “A Novel Technique for Classifying Bird Damage to Rapeseed Plants Based on a Deep Learning Algorithm,” Agronomy, vol. 11, no. 11, p. 2364, 2021, doi: 10.3390/agronomy11112364.

A. Wongpanich et al., “Training EfficientNets at Supercomputer Scale: 83% ImageNet Top-1 Accuracy in One Hour,” 2020, doi: 10.48550/arxiv.2011.00071.

M. A. Hafeez, A. Munir, and H. Ullah, “H-QNN: A Hybrid Quantum–Classical Neural Network for Improved Binary Image Classification,” Ai, vol. 5, no. 3, pp. 1462–1481, 2024, doi: 10.3390/ai5030070.

S. Tas, O. Sari, Y. Dalveren, S. Pazar, A. Kara, and M. Derawi, “Deep Learning-Based Vehicle Classification for Low Quality Images,” Sensors, vol. 22, no. 13, p. 4740, 2022, doi: 10.3390/s22134740.

L. S. Chow, G. S. Tang, M. I. Solihin, N. M. Gowdh, N. Ramli, and K. Rahmat, “Quantitative and Qualitative Analysis of 18 Deep Convolutional Neural Network (CNN) Models With Transfer Learning to Diagnose COVID-19 on Chest X-Ray (CXR) Images,” Sn Comput. Sci., vol. 4, no. 2, 2023, doi: 10.1007/s42979-022-01545-8.

J. Cao, C. Wu, L. Chen, H. Cui, and F. Guo, “An Improved Convolutional Neural Network Algorithm and Its Application in Multilabel Image Labeling,” Comput. Intell. Neurosci., vol. 2019, pp. 1–12, 2019, doi: 10.1155/2019/2060796.

A. Kujur, Z. Raza, A. A. Khan, and C. Wechtaisong, “Data Complexity Based Evaluation of the Model Dependence of Brain MRI Images for Classification of Brain Tumor and Alzheimer’s Disease,” Ieee Access, vol. 10, pp. 112117–112133, 2022, doi: 10.1109/access.2022.3216393.

U. Saxena, “Car vs Bike Classification Dataset.” 2023. [Online]. Available: https://www.kaggle.com/datasets/utkarshsaxenadn/car-vs-bike-classification-dataset/data

N. Zakaria, “Improved Image Classification Task Using Enhanced Visual Geometry Group of Convolution Neural Networks,” Joiv Int. J. Informatics Vis., vol. 7, no. 4, p. 2498, 2023, doi: 10.30630/joiv.7.4.01752.

N. Islam et al., “Diagnosis of Hearing Deficiency Using EEG Based AEP Signals: CWT and Improved-Vgg16 Pipeline,” Peerj Comput. Sci., vol. 7, p. e638, 2021, doi: 10.7717/peerj-cs.638.

S. Kundu, M. Nazemi, M. Pedram, K. M. Chugg, and P. A. Beerel, “Pre-Defined Sparsity for Low-Complexity Convolutional Neural Networks,” Ieee Trans. Comput., p. 1, 2020, doi: 10.1109/tc.2020.2972520.

P. Sudhakar, “Novel Skin Cancer Detection Based Transfer Learning With Optimization Algorithm Using Dermatology Dataset,” Eai Endorsed Trans. Pervasive Heal. Technol., vol. 9, 2023, doi: 10.4108/eetpht.9.4277.

R. Mohan, K. Ganapathy, and A. Rama, “Brain Tumour Classification of Magnetic Resonance Images Using a Novel CNN Based Medical Image Analysis and Detection Network in Comparison With VGG16,” JPTCP, vol. 28, no. 2, 2022, doi: 10.47750/jptcp.2022.873.

F. Yi et al., “Dual Model Medical Invoices Recognition,” Sensors, vol. 19, no. 20, p. 4370, 2019, doi: 10.3390/s19204370.

P. Alkhairi and A. P. Windarto, “Classification Analysis of Back Propagation-Optimized CNN Performance in Image Processing,” J. Syst. Eng. Inf. Technol., vol. 2, no. 1, 2023, doi: 10.29207/joseit.v2i1.5015.

S. K. Lee et al., “Rapid and Concise Quantification of Mycelial Growth by Microscopic Image Intensity Model and Application to Mass Cultivation of Fungi,” Scientific Reports. 2021. doi: 10.1038/s41598-021-03512-4.

D. Varshni, K. Thakral, L. Agarwal, R. Nijhawan, and A. Mittal, “Pneumonia Detection Using CNN Based Feature Extraction,” pp. 1–7, 2019, doi: 10.1109/icecct.2019.8869364.

S. Sumera, R. Sirisha, N. Anjum, and K. Vaidehi, “Implementation of CNN and ANN for Fashion-Mnist-Dataset Using Different Optimizers,” Indian J. Sci. Technol., vol. 15, no. 47, pp. 2639–2645, 2022, doi: 10.17485/ijst/v15i47.1821.

R. Amalia and F. Panjaitan, “Mask Detection Using Convolutional Neural Network Algorithm,” J. Resti (Rekayasa Sist. Dan Teknol. Informasi), vol. 6, no. 4, pp. 639–647, 2022, doi: 10.29207/resti.v6i4.4276.

R. y. Adhitya, “Rancang Bangun Aplikasi Intelligent Visual Scanner Berbasis CNN Untuk Identifikasi Cacat Pada Hasil Pengelasan,” J. Comput. Electron. Telecommun., vol. 4, no. 2, 2023, doi: 10.52435/complete.v4i2.393.

N. Firdausy, “Analisis Sentimen Evaluasi Reaksi E-Learning Menggunakan Algorima Naïve Bayes, Support Vector Machine Dan Deep Learning,” Techno Com, vol. 22, no. 3, pp. 677–689, 2023, doi: 10.33633/tc.v22i3.8160.

V. A. Saputra, “Penerapan Metode Machine Learning Dalam Mengidentifikasi Berita Hoaks,” Comput. Based Inf. Syst. J., vol. 12, no. 1, pp. 112–121, 2024, doi: 10.33884/cbis.v12i1.8442.

A. F. Hanif, “Perbandingan Kinerja LSTM, Bi-Lstm, Dan GRU Pada Klasifikasi Judul Berita Clickbait,” Indones. J. Comput. Sci., vol. 12, no. 4, 2023, doi: 10.33022/ijcs.v12i4.3281.

R. I. Arumnisaa and A. W. Wijayanto, “Comparison of Ensemble Learning Method: Random Forest, Support Vector Machine, AdaBoost for Classification Human Development Index (HDI),” Sistemasi, vol. 12, no. 1, p. 206, 2023, doi: 10.32520/stmsi.v12i1.2501.

B. Imran, “Klasifikasi Berita Hoax Terkait Pemilihan Umum Presiden Republik Indonesia Tahun 2024 Menggunakan Naïve Bayes Dan SVM,” Din. Rekayasa, vol. 20, no. 1, pp. 1–9, 2024, doi: 10.20884/1.dinarek.2024.20.1.27.


Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Perbandingan Efficientnet, Visual Geometry Group 16, dan Residual Network 50 Untuk Klasifikasi Kendaraan Bermotor

Dimensions Badge
Article History
Submitted: 2024-12-14
Published: 2024-12-30
Abstract View: 15 times
PDF Download: 9 times
How to Cite
Andrianto, A., Tahyudin, I., & Karyono, G. (2024). Perbandingan Efficientnet, Visual Geometry Group 16, dan Residual Network 50 Untuk Klasifikasi Kendaraan Bermotor. Building of Informatics, Technology and Science (BITS), 6(3), 1995-2004. https://doi.org/10.47065/bits.v6i3.6450
Issue
Section
Articles