A Detection Algorithm for Two-Wheeled Vehicles in Complex Scenarios Based on Semi-Supervised Learning
Mingen Zhong1, Kaibo Yang1,*, Ziji Xiao1, Jiawei Tan2, Kang Fan2, Zhiying Deng1, Mengli Zhou1
1 Fujian Key Laboratory of Advanced Bus & Coach Design and Manufacture, Xiamen University of Technology, Xiamen, 361024, China
2 School of Aerospace Engineering, Xiamen University, Xiamen, 361102, China
* Corresponding Author: Kaibo Yang. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.063383
Received 13 January 2025; Accepted 28 March 2025; Published online 24 April 2025
Abstract
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.
Keywords
Two wheeled vehicles; illegal behavior detection; object detection; semi supervised learning; deep learning; transformer; convolutional neural network
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