## 2 Related Works  
### Dangerous Driving Behavior Detection  
In recent years, driven by the demand for improved road safety, the detection of dangerous driving behaviors has become a key focus of research. The goal of dangerous driving behavior detection is to assess whether a driver is in a safe driving state to prevent traffic accidents. Various methods have been proposed to address this challenge.  
Vehicle behavior-based dangerous driving behavior detection methods analyze vehicle behavior characteristic parameters to extract features distinguishing normal driving from fatigued driving. These extracted parameters are used to define detection criteria to differentiate between normal and fatigued driving. [PERSON] et al. [22] proposed a Rapidly Adapting Lateral Position Handler (RALPH) system, which analyzes road images using template matching and hypothesis testing strategies to estimate lane departure  
Figure 1: Structure of YOLO-AFR Network. ‘n’ indicates that the module is repeated n times within the overall architecture. ‘2’ indicates that the module is configured to execute sequentially in pairs. ‘C2-weight refers to multiplying a result by channel weights.  
information. This method is non-invasive, allows for easy data collection, and has portable detection equipment with promising market prospects. However, this detection method is susceptible to environmental factors, the driver's skill level, and driving habits.  
Physiological feature-based detection methods involve collecting physiological signals from the driver for detection. [PERSON] et al. [23] developed a low-cost electrocardiogram sensor that collects heart rate variability (HRV) data to detect driver fatigue. While this detection method is highly accurate and robust, it requires the use of wearable devices, which may affect the driver's operations.  
Driver feature-based detection methods assess dangerous driving by analyzing videos of the driver captured by cameras or other image sensors, usually focusing on facial features. In the absence of deep learning solutions, researchers primarily use machine vision techniques such as face recognition and facial landmark localization to extract features and detect fatigued driving. For example, [PERSON] et al. [24] proposed a fatigue detection method based on facial alignment algorithms, utilizing six key points for each eye to locate the driver's eyes and analyzing the eye-opening and closing states using the Adaboost algorithm to determine if the driver is fatigued.  
Deep learning has been applied to driver feature detection tasks. Techniques like Deep Belief Networks (DBN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) have achieved great success in this domain [5]. With the development of R-CNN [25], SSD [26], and YOLO [27; 28; 29; 30; 31; 32; 33; 34; 35] algorithms, an increasing number of researchers have begun applying CNN-based algorithms to detect dangerous driving behaviors. [PERSON] et al. [36]. proposed the Attention R-CNN for accident identification, integrating object class and property detection with attention mechanisms. [PERSON] et al. [37]. introduced an SSD-based method for detecting illegal driving behaviors, demonstrating superior performance over Faster R-CNN. [PERSON] et al. [38] proposed a YOLO-Haar cascade-based method to detect driver sleepiness, incorporating an intelligent agent for real-time feedback.  
Driver feature-based dangerous driving behavior detection methods offer advantages such as low cost, non-contact detection, ease of installation, compact equipment, relatively high accuracy, and the ability to detect driving dangers in real time. However, challenges arise when the driver's head is turned too far, when wearing sunglasses, or when there is insufficient lighting, which can result in detection failures. Despite these issues, these detection methods remain dominant in the field, with a promising outlook.