# Enhanced YOLO Network for Improving the Efficiency of Traffic Sign Detection
## 1 Introduction  
The detection and recognition of traffic signs has the potential to significantly improve vehicular environment perception. In the context of road navigation, the ability to acquire precise, real-time traffic sign information stands as a pivotal asset. Recent advancements in research have prominently harnessed the capabilities of deep learning techniques, with a notable emphasis on leveraging Convolutional Neural Networks (CNNs) [1, 2]. These techniques have been adeptly applied to extract features that facilitate the tasks of detection and classification. The typical workflow involves training the network on an annotated dataset and subsequently subjecting the model to validation and testing phases specifically tailored for traffic sign detection. Presently, two distinct strategies have emerged as prominent avenues for object detection: the region-suggestion-based strategy and the regression-based strategy. The former strategy adheres to conventional object detection paradigms, generating and categorizing region proposals. Within this category, notable methodologies include R-CNN [3], SPPNet [4], Fast-RCNN [5], Mask-RCNN [6], and Faster-RCNN [7]. [PERSON] et al. [8] introduced the AT attention network within the framework of Faster R-CNN, enabling the precise identification of regions of interest. Subsequently, the utilization of a fine region proposal network (FRPN) yielded exceptional performance with an 80.31% mAP score on the TT100K dataset [9]. Additionally, [PERSON] et al. [10] pioneered the use of a visual attention model, which tactically focuses exclusively on specific regions, obviating the need to process the entire image for detection. Noteworthy contributions also include the cascaded network approach by [PERSON] et al. [11], which uses the R-ANN model for joint training and multi-scale capabilities. The concept of a pyramid network characteristic was introduced by [PERSON] et al. [9], adeptly addressing multi-scale object detection. This architecture comprises two principal components: a multi-scale pyramidal hierarchy drawn from CNN's inherent structure and a top-down network structure featuring horizontal connections. By amalgamating high-level feature maps across scales, each containing semantic insights, this approach ascertains a universal feature extractor, which is a significant advancement. Further elevating the field, [PERSON] et al. [12] adopted the Res2 Net [13] network architecture, serving as Faster R-CNN's foundation. This innovation extended to the refinement of the original Non-Maximum Suppression (NMS) calculation method, utilizing both confidence and intersection over union (IOU) as evaluation criteria. The outcome was a remarkably improved bounding box generation accuracy, culminating in an impressive 87.4% average precision (AP). In summation, recent research within the realm of traffic sign detection has been characterized by the proficient deployment of deep learning, particularly hinged upon CNNs. This approach, bolstered by an array of innovative strategies and architectures, has garnered remarkable advancements in both accuracy and efficacy across various benchmark datasets. Building upon the insights gleaned from the aforementioned literature, in this research, we describe a novel CNN-based architecture for traffic sign detection. Our work is motivated by the aspiration to contribute to the field and expand its horizons. The central contributions of this research are articulated as follows:  
1. Enhanced K-means++ Anchor Frame Clustering Algorithm: We carefully re-cluster the dataset using an enhanced version of the K-means++ anchor frame clustering technique. The objective is to derive initial candidate frames that are notably more accurate, thus priming our network for improved performance. 2. Refined Backbone Network Structure and H-SPPF Module: The structure of our backbone network is strategically revised to prioritize the detection of diminutive objects. Additionally, we propose a hybrid spatial pyramidal pooling H-SPPF module.