# An Improved YOLO Model for Traffic Signs Small Target Image Detection
## 1 Introduction  
Research on traffic sign object detection can be classified into two categories: traditional methods [1] and deep learning-based methods. Traditional techniques for traffic sign detection encompass color-based detection and shape-based detection. However, color-based detection can be compromised by the fading or absence of color in traffic signs, resulting in decreased detection performance [2]. On the other hand, shape-based traffic sign detection leverages the extraction of edge information to analyze the shape and category of traffic signs. While shape-based detection is less susceptible to color decay, its performance also deteriorates when there are variations in the shape or detection angle of the sign. Deep learning-based algorithms demonstrate superior real-time performance and accuracy compared to traditional traffic detection algorithms. Since the introduction of Region-Based Convolutional Neural Networks (R-CNN) in 2014 [3], deep learning has gained significant traction in traffic sign detection. Deep learning-based object detection algorithms can be categorized into two main directions: one-stage and two-stage detection algorithms. One-stage detection algorithms, such as the You Only Look Once (YOLO)series [4], EfficientDet [5], and Single Shot MultiBox Detector (SSD) [6], provide efficient and accurate detections in a single pass. On the other hand, two-stage detection algorithms, such as Fast Region-Based Convolutional Neural Networks (Fast R-CNN) [7], Faster Region-Based Convolutional Neural Networks (Faster R-CNN) [8], Spatial Pyramid Pooling in deep convolutional networks (SPP-Net) [9], and HyperNet [10] do exhibit exceptional robustness, but they mandate a more intricate process that unfolds across multiple stages for a comprehensive detection. These advancements in deep learning-based algorithms have significantly contributed to the field of traffic sign detection. Researchers have made significant progress in applying deep learning-based object detection algorithms to traffic sign detection [11; 12; 13]. [PERSON] and colleagues established a new benchmark for traffic sign detection, the TsinghuaTencent100K (TT100K) [14], which comprises a larger image dataset, higher resolution, and more complex backgrounds compared to the widely-used German Traffic Sign Detection Benchmark (GTSDB) [15]. Many studies are currently being conducted using this benchmark. [PERSON] and colleagues introduced a novel approach focusing on enhancing multi-scale detection performance [16]. Their method integrates Dilated Spatial Pyramid Pooling (DSPP) and the Multi-scale Feature Residual Aggregation Module (FRAM) to aggregate features across various scales [17]. However, despite achieving an impressive mean accuracy with their approach, they still encountered challenges in precisely detecting small traffic signs. [PERSON] [18] proposed a deep learning-based real-time traffic sign recognition system for autonomous driving in urban environments. Their method employed the YOLOv5 detector and the strongSORT tracking model [19], both of which surpassed other models in terms of accuracy and processing time. Furthermore, they extensively discussed potential challenges in the traffic sign recognition task. However, despite their method's significant advancements under various road conditions, it exhibited a distinct decrease in performance when detecting small traffic signs, especially those with lower resolution in images. [PERSON] and his team [20] developed a model with global feature extraction capabilities and incorporated a multi-branch lightweight detection head to enhance the detection accuracy of small traffic signs. However, even though their algorithm achieved an accuracy of 86.3% on the TT100K dataset, its performance still lags behind other state-of-the-art methods. [PERSON] and associates put forth a paradigm for traffic sign detection and recognition based on deep learning algorithms [21].