A Study on Fine-Grained Motorcycle Classification for Intelligent Transportation Systems
Resumo
Vehicle recognition from images is crucial to Intelligent Transportation System (ITS), supporting applications such as tolling, access control, and forensics. Fine-Grained Vehicle Classification (FGVC) extends this capability by identifying vehicles by make, model, and type. However, research has largely centered on four-wheeled vehicles, with motorcycles receiving limited attention despite representing a substantial share of traffic in many countries, including Brazil. This under-representation can reduce ITS effectiveness and fairness. This work-in-progress study addresses this gap by investigating Fine-Grained Motorcycle Classification (FGMC) in real-world ITS scenarios. We evaluate seven deep learning architectures under two training protocols for independent make and model classification. To enable this, we augment a widely adopted dataset for Automatic License Plate Recognition (ALPR) with motorcycle make and model annotations. Results show that FGMC is feasible within the studied context, yet performance is hindered by severe class imbalance, underscoring the need for improved balancing strategies. The results also reveal a drop in accuracy under challenging conditions, particularly at night or in low-light environments. Future directions include expanding the dataset to more diverse scenarios and exploring FGMC integration with ALPR to enhance overall vehicle identification accuracy.Referências
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I. O. Oliveira et al., “Vehicle-Rear: A new dataset to explore feature fusion for vehicle identification using convolutional neural networks,” IEEE Access, vol. 9, pp. 101 065–101 077, 2021.
V. Nascimento et al., “Toward advancing license plate super-resolution in real-world scenarios: A dataset and benchmark,” Journal of the Brazilian Computer Society, vol. 1, no. 31, pp. 435–449, 2025.
L. Yang, P. Luo, C. C. Loy, and X. Tang, “A large-scale car dataset for fine-grained categorization and verification,” in IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 3973–3981.
J. Krause, J. Deng, M. Stark, and L. Fei-Fei, “Collecting a large-scale dataset of fine-grained cars,” [link], 2013.
J. Masula, R. Tingga, J. A. Salido, J. C. Española, and G. K. Gillesania, “Motorcycle recognition system using convolutional neural network,” Journal of Innovative Technology Convergence, vol. 6, no. 3, 2024.
B. Hu, J.-H. Lai, and C.-C. Guo, “Location-aware fine-grained vehicle type recognition using multi-task deep networks,” Neurocomputing, vol. 243, pp. 60–68, 2017.
J. E. Espinosa, S. A. Velastin, and J. W. Branch, “Motorcycle classification in urban scenarios using convolutional neural networks for feature extraction,” in International Conference of Pattern Recognition Systems (ICPRS), 2017, pp. 1–6.
P. K. Khoba, C. Parikh, C. Jawahar, R. K. Sarvadevabhatla, and R. Saluja, “A fine-grained vehicle detection (FGVD) dataset for unconstrained roads,” in Indian Conference on Computer Vision, Graphics and Image Processing, 2023.
S. M. Mansoor Roomi, V. Lokesh, S. Mahadevan G, and K. Priya, “A transfer learning approach for identification of two-wheeler brand and model,” Computación y Sistemas, vol. 28, no. 2, pp. 543–551, 2024.
Associação Brasileira dos Fabricantes de Motocicletas, Ciclomotores, Motonetas, Bicicletas e Similares, “Two wheel sector data 2024,” [link], 2025, accessed: 2025-08-10.
Fantástico, “Fantástico apresenta raio-x inédito com o mapa dos roubos e furtos de moto em são paulo,” [link], 2025, accessed: 2025-08-10.
L. Wojcik, G. E. Lima, V. Nascimento, E. Nascimento Jr., R. Laroca, and D. Menotti, “LPLC: A dataset for license plate legibility classification,” Conference on Graphics, Patterns and Images, pp. 1–6, 2025.
X. Clady, P. Negri, M. Milgram, and R. Poulenard, “Multi-class vehicle type recognition system,” in Artificial Neural Networks in Pattern Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008, pp. 228–239.
V. Petrovic and T. Cootes, “Analysis of features for rigid structure vehicle type recognition,” in British Machine Vision Conference (BMVC), vol. 2, 2004.
R. Laroca, E. V. Cardoso, D. R. Lucio, V. Estevam, and D. Menotti, “On the cross-dataset generalization in license plate recognition,” in International Conference on Computer Vision Theory and Applications (VISAPP), Feb 2022, pp. 166–178.
Q. Liu, Y. Liu, S.-L. Chen, T.-H. Zhang, F. Chen, and X.-C. Yin, “Improving multi-type license plate recognition via learning globally and contrastively,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 9, pp. 11 092–11 102, 2024.
G. E. Lima, R. Laroca, E. Santos, E. Nascimento Jr., and D. Menotti, “Toward enhancing vehicle color recognition in adverse conditions: A dataset and benchmark,” in Conference on Graphics, Patterns and Images (SIBGRAPI), Sept 2024, pp. 1–6.
V. Nascimento et al., “Enhancing license plate super-resolution: A layout-aware and character-driven approach,” Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1–6, 2024.
R. Laroca, L. A. Zanlorensi, V. Estevam, R. Minetto, and D. Menotti, “Leveraging model fusion for improved license plate recognition,” in Iberoamerican Congress on Pattern Recognition, Nov 2023, pp. 60–75.
Ultralytics, “Ultralytics yolo11,” [link], 2025, accessed: 2025-08-10.
T. Diwan, G. Anirudh, and J. V. Tembhurne, “Object detection using YOLO: Challenges, architectural successors, datasets and applications,” Multimedia Tools and Applications, vol. 82, no. 6, pp. 9243–9275, 2023.
J. Terven, D.-M. Córdova-Esparza, and J.-A. Romero-González, “A comprehensive review of YOLO architectures in computer vision: From YOLOv1 to YOLOv8 and YOLO-NAS,” Machine Learning and Knowledge Extraction, vol. 5, no. 4, pp. 1680–1716, 2023.
R. Laroca, M. dos Santos, and D. Menotti, “Improving small drone detection through multi-scale processing and data augmentation,” in International Joint Conference on Neural Networks (IJCNN), 2025.
V. Montoia, “Lista: veja quais foram as motos mais vendidas do brasil até julho 2025,” [link], 2025, accessed: 2025-08-10.
M. Tan and Q. Le, “EfficientNetV2: Smaller models and faster training,” in International Conf. on Machine Learning, 2021, pp. 10 096–10 106.
A. Howard et al., “Searching for MobileNetV3,” in IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 1314–1324.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
Z. Liu, H. Hu, Y. Lin, Z. Yao, Z. Xie, Y. Wei, J. Ning, Y. Cao, Z. Zhang, L. Dong, F. Wei, and B. Guo, “Swin transformer v2: Scaling up capacity and resolution,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 11 999–12 009.
A. Dosovitskiy et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” in International Conference on Learning Representations (ICLR), 2021, pp. 1–22.
Ultralytics, “YOLOv11 Image Classification,” [link], 2025, accessed: 2025-08-10.
——, “Data augmentation using Ultralytics YOLO,” [link], 2025, accessed: 2025-08-10.
E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, “Randaugment: Practical automated data augmentation with a reduced search space,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020, pp. 3008–3017.
R. Laroca, V. Estevam, A. S. Britto Jr., R. Minetto, and D. Menotti, “Do we train on test data? The impact of near-duplicates on license plate recognition,” in International Joint Conference on Neural Networks (IJCNN), June 2023, pp. 1–8.
F. Wilcoxon, Individual Comparisons by Ranking Methods. New York, NY: Springer New York, 1992, pp. 196–202. [Online]. DOI: 10.1007/978-1-4612-4380-9_16
J. Demšar, “Statistical comparisons of classifiers over multiple data sets,” J. Mach. Learn. Res., vol. 7, p. 1–30, Dec. 2006.
Publicado
30/09/2025
Como Citar
LIMA, Gabriel E.; SANTOS, Eduardo; NASCIMENTO JR., Eduil; LAROCA, Rayson; MENOTTI, David.
A Study on Fine-Grained Motorcycle Classification for Intelligent Transportation Systems. In: WORKSHOP DE TRABALHOS EM ANDAMENTO - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 38. , 2025, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 162-167.
