Performance Benchmarking: Pre-trained Models and Custom Convolutional Neural Networks in Deep Learning
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Abstract
Recent advances in computer vision and deep learning, particularly Convolutional Neural Networks (CNNs), have significantly increased road safety. CNNs were used in this work to automatically detect and categorise traffic signs—a crucial task for autonomous vehicles (AVs) and advanced driver assistance systems (ADAS). These technologies' ability to accurately recognize traffic signs enables them to make informed decisions in real time, thereby elevating the standard for overall driving safety. The study uses a large, annotated dataset of images of traffic signs to train and assess the CNN model. We developed a model that can recognize a large number of traffic lights, even in challenging scenarios such as low light levels, adverse weather, or high traffic. CNN image processing enables the system to accurately recognize and categorize traffic signs. Real-time predictions made by the CNN model after training aid ADAS and autonomous vehicles in comprehending road conditions. Real-time recognition is essential for tasks like managing turns, stopping at red lights, and adhering to speed restrictions. The research also addresses real-world challenges to ensure the model performs effectively in light or weather changes. A thorough testing process validates the model's accuracy and reliability. Ultimately, this technology might significantly increase road safety by providing drivers with more precise information, improving ADAS and AV decision-making skills, and reducing the number of accidents caused by drivers misinterpreting traffic signals.
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References
M. Lamrini, M. Y. Chkouri, and A. Touhafi, “Evaluating the performance of pre-trained convolutional neural network for audio classification on embedded systems for anomaly detection in smart cities,” Sensors, vol. 23, p. 6227, 2023. doi: 10.3390/s23136227.
F. A. Abdulazeez, I. T. Ahmed, and B. T. Hammad, “Examining the performance of various pretrained convolutional neural network models in malware detection,” Appl. Sci., vol. 14, no. 6, p. 2614, Mar. 2024. doi: 10.3390/app14062614.
T. Shahzad and K. Aman, “Unveiling the efficacy of AI-based algorithms in phishing attack detection,” JIWE, vol. 3, no. 2, pp. 116–133, Jun. 2024. doi: 10.33093/jiwe.2024.3.2.9
Y. H.-S. Kam, K. Jones, R. Rawlinson-Smith, and K. Tam, “In search of suitable methods for cost-benefit analysis of cyber risk mitigation in offshore wind: A survey,” JIWE, vol. 3, no. 3, pp. 314–328, Oct. 2024. doi: /10.33093/jiwe.2024.3.3.20
X. R. Lim et al., “Recent advances in traffic sign recognition: approaches and datasets,” Sensors, vol. 23, no. 10, p. 4674, May 2023. doi: 10.3390/s23104674.
P. Kuppusamy, M. Sanjay, P. V. Deepashree, and C. Iwendi, “Traffic sign recognition for autonomous vehicle using optimized YOLOv7 and convolutional block attention module,” Computers, Materials & Continua, vol. 77, no. 1, pp. 445–466, Jan. 2023. doi: 10.32604/cmc.2023.042675.
W. Qayyum et al., “Assessment of convolutional neural network pre-trained models for detection and orientation of cracks,” Materials, vol. 16, no. 2, p. 826, Jan. 2023. doi: 10.3390/ma16020826.
M. Vashisht and B. Kumar, “Effective implementation of machine learning algorithms using 3D colour texture feature for traffic sign detection for smart cities,” Expert Syst., vol. 39, no. 5, Aug. 2021. doi: 10.1111/exsy.12781.
Y. Wang and S. H. Chung, “Artificial intelligence in safety-critical systems: a systematic review,” Ind. Manag. Data Syst., vol. 122, no. 2, pp. 442–470, Dec. 2021. doi: 10.1108/imds-07-2021-0419.
Y. Jia, J. McDermid, T. Lawton, and I. Habli, “The role of explainability in assuring safety of machine learning in healthcare,” IEEE Trans. Emerg. Top. Comput., vol. 10, no. 4, pp. 1746–1760, May 2022. doi: 10.1109/TETC.2022.3171314.
J. Perez-Cerrolaza et al., “Artificial intelligence for safety-critical systems in industrial and transportation domains: A survey,” ACM Comput. Surv., vol. 56, no. 7, pp. 1–40, Oct. 2023. doi: 10.1145/3626314.
K. Muhammad, A. Ullah, J. Lloret, J. Del Ser, and V. H. C. De Albuquerque, “Deep learning for safe autonomous driving: current challenges and future directions,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 7, pp. 4316–4336, Dec. 2020. doi: 10.1109/TITS.2020.3032227.
R. Archana and P. S. E. Jeevaraj, “Deep learning models for digital image processing: a review,” Artif. Intell. Rev., vol. 57, no. 1, Jan. 2024. doi: 10.1007/s10462-023-10631-z.
G. Montavon, W. Samek, and K.-R. Muller, “Methods for interpreting and understanding deep neural networks,” Digit. Signal Process., vol. 73, pp. 1–15, Oct. 2017. doi: 10.1016/j.dsp.2017.10.011.
M. M. Ansari et al., “Evaluating CNN architectures and hyperparameter tuning for enhanced lung cancer detection using transfer learning,” J. Electr. Comput. Eng., vol. 2024, no. 1, Jan. 2024. doi: 10.1155/2024/3790617.
Y. An, C. Yang, and S. Zhang, “A lightweight network architecture for traffic sign recognition based on enhanced LeNet-5 network,” Front. Neurosci., vol. 18, Jun. 2024. doi: 10.3389/fnins.2024.1431033.
Z. He, F. Nan, X. Li, S. Lee, and Y. Yang, “Traffic sign recognition by combining global and local features based on semi-supervised classification,” IET Intell. Transp. Syst., vol. 14, no. 5, pp. 323–330, Oct. 2019. doi: 10.1049/iet-its.2019.0409.
R. Fan and M. Liu, “Road damage detection based on unsupervised disparity map segmentation,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 11, pp. 4906–4911, Nov. 2019. doi: 10.1109/TITS.2019.2947206.
R. Fan, H. Wang, Y. Wang, M. Liu, and I. Pitas, “Graph attention layer evolves semantic segmentation for road pothole detection: a benchmark and algorithms,” IEEE Trans. Image Process., vol. 30, pp. 8144–8154, Jan. 2021. doi: 10.1109/TIP.2021.3112316.
G. Rangel, J. C. Cuevas-Tello, J. Nunez-Varela, C. Puente, and A. G. Silva-Trujillo, “A survey on convolutional neural networks and their performance limitations in image recognition tasks,” J. Sens., vol. 2024, no. 1, Jan. 2024. doi: 10.1155/2024/2797320.
O. A. H. Almaswari, G. C. Chung, I. E. Lee, and K. V. Teong, “Deep learning based security system using car plate information,” in Recent Advancements in Engineering and Technology, V. T. Goh and J. J. Tiang, Eds. MMU Press, 2023, pp. 193–206.