Zhou Dan, Zhao Zhenzhong, Yang Ruixin, Huang Shiqian, Wu Zhilong
School of Architecture and Transportation Engineering, Guilin University of Electronic Technology, Guilin 541004, China.
Guangxi Key Laboratory of ITS, Guilin University of Electronic Technology, Guilin 541004, China.
Sensors (Basel). 2024 Jan 24;24(3):759. doi: 10.3390/s24030759.
Two-wheeled non-motorized vehicles (TNVs) have become the primary mode of transportation for short-distance travel among residents in many underdeveloped cities in China due to their convenience and low cost. However, this trend also brings corresponding risks of traffic accidents. Therefore, it is necessary to analyze the driving behavior characteristics of TNVs through their trajectory data in order to provide guidance for traffic safety. Nevertheless, the compact size, agile steering, and high maneuverability of these TNVs pose substantial challenges in acquiring high-precision trajectories. These characteristics complicate the tracking and analysis processes essential for understanding their movement patterns. To tackle this challenge, we propose an enhanced You Only Look Once Version X (YOLOx) model, which incorporates a median pooling-Convolutional Block Attention Mechanism (M-CBAM). This model is specifically designed for the detection of TNVs, and aims to improve accuracy and efficiency in trajectory tracking. Furthermore, based on this enhanced YOLOx model, we have developed a micro-trajectory data mining framework specifically for TNVs. Initially, the paper establishes an aerial dataset dedicated to the detection of TNVs, which then serves as a foundational resource for training the detection model. Subsequently, an augmentation of the Convolutional Block Attention Mechanism (CBAM) is introduced, integrating median pooling to amplify the model's feature extraction capabilities. Subsequently, additional detection heads are integrated into the YOLOx model to elevate the detection rate of small-scale targets, particularly focusing on TNVs. Concurrently, the Deep Sort algorithm is utilized for the precise tracking of vehicle targets. The process culminates with the reconstruction of trajectories, which is achieved through a combination of video stabilization, coordinate mapping, and filtering denoising techniques. The experimental results derived from our self-constructed dataset reveal that the enhanced YOLOx model demonstrates superior detection performance in comparison to other analogous methods. The comprehensive framework accomplishes an average trajectory recall rate of 85% across three test videos. This significant achievement provides a reliable method for data acquisition, which is essential for investigating the micro-level operational mechanisms of TNVs. The results of this study can further contribute to the understanding and improvement of traffic safety on mixed-use roads.
两轮非机动车（TNVs）因其便捷性和低成本，已成为中国许多欠发达城市居民短途出行的主要交通方式。然而，这一趋势也带来了相应的交通事故风险。因此，有必要通过其轨迹数据来分析两轮非机动车的驾驶行为特征，以便为交通安全提供指导。尽管如此，这些两轮非机动车体积小巧、转向灵活且机动性高，在获取高精度轨迹方面带来了巨大挑战。这些特性使理解其运动模式所必需的跟踪和分析过程变得复杂。为应对这一挑战，我们提出了一种增强版的You Only Look Once Version X（YOLOx）模型，该模型融合了中值池化-卷积块注意力机制（M-CBAM）。此模型专为两轮非机动车的检测而设计，旨在提高轨迹跟踪的准确性和效率。此外，基于这一增强版的YOLOx模型，我们开发了一个专门用于两轮非机动车的微观轨迹数据挖掘框架。本文首先建立了一个专门用于两轮非机动车检测的航空数据集，该数据集随后作为训练检测模型的基础资源。随后，引入了卷积块注意力机制（CBAM）的增强版本，集成中值池化以增强模型的特征提取能力。接着，将额外的检测头集成到YOLOx模型中，以提高小尺度目标的检测率，尤其关注两轮非机动车。同时，利用深度排序（Deep Sort）算法对车辆目标进行精确跟踪。该过程最终通过视频稳定、坐标映射和滤波去噪技术相结合实现轨迹重建。从我们自建数据集中得出的实验结果表明，与其他类似方法相比，增强版的YOLOx模型展现出卓越的检测性能。这个综合框架在三个测试视频中实现了平均85%的轨迹召回率。这一显著成果提供了一种可靠的数据采集方法，这对于研究两轮非机动车的微观运行机制至关重要。本研究结果可进一步有助于理解和改善混合用途道路上的交通安全。
