A Detection Algorithm for Two-Wheeled Vehicles in Complex Scenarios Based on Semi-Supervised Learning

• Published in: Computers, materials & continua Vol. 84; no. 1; pp. 1055 - 1071
• Main Authors: Zhong, Mingen, Yang, Kaibo, Xiao, Ziji, Tan, Jiawei, Fan, Kang, Deng, Zhiying, Zhou, Mengli
• Format: Journal Article
• Language: English
• Published: Henderson Tech Science Press 2025
• Subjects: Accuracy; Algorithms; Cost effectiveness; Efficiency; Image processing; Monitoring; Population growth; Real time; Semi-supervised learning; Traffic safety; Traffic violations; Urban environments; Vehicle identification; Vehicles
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2025.063383

With the rapid urbanization and exponential population growth in China, two-wheeled vehicles have become a popular mode of transportation, particularly for short-distance travel. However, due to a lack of safety awareness, traffic violations by two-wheeled vehicle riders have become a widespread concern, contributing to urban traffic risks. Currently, significant human and material resources are being allocated to monitor and intercept non-compliant riders to ensure safe driving behavior. To enhance the safety, efficiency, and cost-effectiveness of traffic monitoring, automated detection systems based on image processing algorithms can be employed to identify traffic violations from eye-level video footage. In this study, we propose a robust detection algorithm specifically designed for two-wheeled vehicles, which serves as a fundamental step toward intelligent traffic monitoring. Our approach integrates a novel convolutional and attention mechanism to improve detection accuracy and efficiency. Additionally, we introduce a semi-supervised training strategy that leverages a large number of unlabeled images to enhance the model’s learning capability by extracting valuable background information. This method enables the model to generalize effectively to diverse urban environments and varying lighting conditions. We evaluate our proposed algorithm on a custom-built dataset, and experimental results demonstrate its superior performance, achieving an average precision (AP) of 95% and a recall (R) of 90.6%. Furthermore, the model maintains a computational efficiency of only 25.7 GFLOPs while achieving a high processing speed of 249 FPS, making it highly suitable for deployment on edge devices. Compared to existing detection methods, our approach significantly enhances the accuracy and robustness of two-wheeled vehicle identification while ensuring real-time performance.