# An Improved YOLO Model for Traffic Signs Small Target Image Detection
## 1 Introduction
Their approach leverages parallel deformable convolution modules, sub-pixel convolution attention modules, and GSConv [22] to boost the model's performance. While their method marked significant advancements in handling traffic sign object detection, small object detection accuracy still fell short. Although researchers have made significant progress in applying deep learning-based object detection algorithms to traffic sign detection [23; 24], practical detection still faces challenges, particularly due to the prevalence of low-resolution small targets in traffic signs (as shown in Figure 1) [25], resulting in low detection accuracy and missed detections. Therefore, further research is necessary to improve traffic sign detection algorithms. Enhancing the accuracy of detecting small targets can improve overall detection performance since small targets constitute a significant proportion of traffic signs. This study aims to enhance the accuracy and recall rate of small targets in traffic signs, thereby improving overall detection performance and facilitating practical applications. Figure 1: Examples of low-resolution traffic sign small object images in real-world scenarios. The advantages of our proposed technique lie in its ability to improve the detection accuracy and localization of small traffic sign objects in real-world scenarios. The main contributions of this work can be summarized as follows:  
1. In order to enhance the detection accuracy and accelerate network convergence within the YOLOv5 architecture, we introduce a decoupled head from YOLOX [26] into the detection head of the model. The underlying concept behind this proposal is to segregate the classification and localization tasks, thereby ensuring a more focused and specialized tackle on each sub-task in the detection process. This incorporates a decoupled architecture approach that caters to the model's need to separately optimize these distinct detection aspects. This innovative inclusion promises to increase the precision of detection while accelerating the model's learning efficiency. 2. We propose to replace the original convolution in the network framework with the SPD-Convolution module. This change aims to eliminate the impact of pooling operations on feature information and enhance the model's ability to extract features from low-resolution small objects. 3. Adding the Contextual Augmentation Module (CAM) to supplement contextual information by utilizing extended convolutions with varying rates. This module captures contextual information from different receptive fields, playing a crucial complementary role in small object detection of traffic signs. It effectively addresses the challenges posed by small targets and improves detection accuracy. 4. Our proposed technique significantly reduces the occurrence of missed detections and false positives, ensuring a high recall rate of 91.6% and a precision rate of 95.0%. These results represent substantial improvements over the original YOLOv5 algorithm, demonstrating the efficacy of our approach in achieving more precise and comprehensive traffic sign detection. The remainder of this paper is organized as follows. Section 2 provides a detailed description of our proposed method. In Sections 3 and 4, we present the experimental setup and evaluation results, demonstrating the effectiveness of our algorithm. Finally, Section 5 concludes the paper and discusses future research directions.