## 1 Introduction  
Over the past few decades, autonomous driving technology has emerged as a focal point in the research of intelligent transportation systems and robotics. Within this domain, target detection plays a crucial role, serving as one of the key elements to ensure vehicular safety. In the realm of autonomous driving research, the recognition of multi-scale targets is not only highly challenging but also a critical task for ensuring the precision and reliability of the system. The development of target detection technology can be generally divided into two key phases: traditional methods and deep learning-based methods. In classical target detection, the Viola-Jones (VJ) detector, introduced by [PERSON] and [PERSON], marked a significant milestone as a major breakthrough in the field [1, 2]. Furthermore, the Deformable Part Models (DPMs) proposed by [PERSON], and later improved by [PERSON], once dominated the detection challenges in VOC 2007, 2008, and 2009 [3, 4, 5]. Due to the swift advancement in deep learning, especially with the widespread implementation of Convolutional Neural Networks (CNNs), the target detection field has experienced a significant transformation. In this revolution, [PERSON] and others introduced the Region-based Convolutional Neural Network (R-CNN) in 2016, incorporating CNN features and bringing a fresh perspective to the domain of target detection [6]. Subsequent advancements, such as Fast R-CNN [7] and Faster R-CNN [8], improved detection precision and significantly increased processing speed while reducing computational costs by integrating the Region Proposal Network (RPN). In the field of target detection technology, the introduction of one-stage detectors such as YOLO [9] and SSD [10] represents significant technological breakthroughs. The advent of RetinaNet further propelled the development in this domain, as it effectively addressed the issue of class imbalance through the adoption of Focal Loss technology, significantly enhancing the model's efficiency and accuracy in processing various types of samples [11]. Moreover, RetinaNet conducts object detection at multiple feature levels, further improving detection performance. The evolution of the YOLO series has been significant in target detection technology. Beginning with YOLOv2 [12] and YOLOv3 [13], the introduction of anchor boxes and multi-scale detection greatly improved the model's accuracy for small objects and complex backgrounds. YOLOv4 [14] and YOLOv5 [15] introduced further innovations, utilizing efficient feature extractors, attention modules, and data augmentation strategies to improve the model's precision and robustness. Subsequently, variants like YOLO-R [16], YOLOv5 [17], YOLOv6 [18], and YOLOv7 [19] continued to refine the network architecture and algorithms. Particularly in the domain of autonomous driving, these models ensured safe and efficient driving by quickly and accurately identifying and locating vehicles, pedestrians, and traffic signs. The latest, YOLOv8 [20], has further increased detection speed and accuracy, demonstrating the maturity and optimization potential of deep learning in practical applications. Recent progress in object detection has resulted in the creation of more efficient and precise algorithms, particularly within the realms of autonomous driving and traffic monitoring. [PERSON] and [PERSON] (2022) developed an improved YOLOv5 network tailored for automatic driving scene target detection, achieving notable accuracy in complex driving scenarios [21]. [PERSON] et al. (2022) investigated the application of standard vision transformer backbones for object detection, contributing to advancements in transformer-based models [22]. [PERSON] and [PERSON] (2022) utilized deep learning techniques for detecting and tracking moving vehicles at roundabouts, demonstrating the efficacy of trajectory union methods [23].