## 1 Introduction  
In recent years, with the rapid growth of global motor vehicle ownership and the continuous expansion of the transportation and logistics industries, the number of traffic accidents has increased significantly. According to the World Health Organization, around 1.35 million people die each year as a result of road traffic accidents, and this number continues to rise annually [1]. Numerous studies have shown that human factors account for nearly 90% of traffic accidents [2], among which dangerous driving behaviors, such as single-handed driving, drinking, distracted driving, drowsiness, and speeding, are the main contributors. These behaviors compromise driver concentration and situational awareness, severely threatening public safety and social development [3]. With the advent of intelligent transportation systems and the growing popularity of electric vehicles and advanced driver assistance systems (ADAS), real-time monitoring and detection of dangerous driving behaviors have become crucial for ensuring road safety [4]. Dangerous driving behavior detection refers to the use of technological means to identify drivers' irregular or risky actions, including abnormal maneuvers, distraction, and physiological fatigue. This process aims to intervene promptly, provide warnings, and ultimately reduce the occurrence of traffic accidents [5]. Current approaches to identifying dangerous driving behaviors are broadly categorized as indirect or direct [6]. Indirect methods, relying on vehicle dynamics data [7,8], can be unreliable due to external factors. Direct methods monitor the driver, but physiological sensors [9] may be intrusive or uncomfortable. Consequently, behavior-based detection using computer vision has emerged as the mainstream research direction. This approach leverages deep learning to analyze driving imagery for risky behaviors (e.g., phone use, smoking, drowsiness) [10, 11, 12], offering a non-intrusive, adaptable, and scalable solution for real-world scenarios. For the dangerous driving behavior detection task discussed in this article, among these CNN models, those based on YOLO and its improvements have demonstrated excellent performance [13, 14, 15, 16]. [PERSON] et al. [17] proposed YOLO-SGC, a dangerous driving behavior detection method built upon YOLOv8, enhanced with SC-Conv and GAM for multiscale spatial-channel feature aggregation, and optimized SPPF with CSPCs, achieving state-of-the-art results at the time. While these advanced models show considerable promise, they often present a trade-off. Some, like YOLO-SGC, achieve high detection accuracy at the cost of reduced processing speed (FPS). Conversely, other models are optimized for high efficiency and speed; for example, lightweight approaches such as CoP-YOLO [18] can achieve high FPS but may exhibit compromised accuracy for certain detection categories. Striking an optimal balance between high accuracy across all driving behaviors and real-time computational performance remains a significant challenge in the field. The complexity and diversity of dangerous driving behaviors present significant challenges to detection models. Factors such as individual driver differences, different driving environments, and perspectives can all impact detection performance. Additionally, overlapping and small target behaviors, such as brief phone usage or subtle signs of fatigue, require models with strong feature extraction capabilities and robustness. As a result, continuous improvement of detection algorithms, including optimizing network architecture, enhancing feature fusion mechanisms, and incorporating attention modules, has become a key research direction. In this context, our study aims to further advance the field of dangerous driving behavior detection by designing a high-precision model without compromising performance. Specifically, we focus on improving model efficiency and detection precision, enabling the system to be flexibly deployed in real driving environments without compromising computational cost.