Improved small target detection algorithm based on multiattention and YOLOv5s for traffic sign recognition

Abstract

Traffic sign detection and recognition facilitates real-time monitoring and interpretation of various traffic signs on the road, such as those indicating speed limits, prohibition of overtaking, and navigation cues. This has substantial applications for autonomous driving and decision-making systems. Consequently, designing accurate and efficient algorithms for the automatic recognition of traffic signs is crucial in the intelligent transportation field. However, targets that need to be detected by traffic sign recognition applications are mostly small-sized, causing challenges regarding their automatic recognition. The YOLOv5s model, characterized by its minimal depth and narrowest feature map, has gained widespread popularity for executing detection owing to its features of being lightweight and easily portable. Furthermore, the YOLOv5s model uses an anchor-based prediction approach that uses anchor boxes of different sizes and shapes to regress and classify various targets. This method generates dense anchor boxes and enables the model to directly perform object classification and bounding box regression, thereby enhancing its target recall capability. Therefore, the anchor-based Yolov5s method has been applied to traffic sign detection; however, it suffers from issues such as false positives and missed detection. Detection of small targets continues to be a challenging aspect in current traffic sign recognition technology due to the following: small targets carry less information; detection of small targets requires high precision in positioning; and environmental noise may overwhelm the detection of small targets. To overcome the abovementioned issues, such as missed detection, false positives, and low detection accuracy, this study proposes a model called STD-YOLOv5s that is specifically designed for small target detection. First, by increasing the number of upsampling and prediction output layers, this model obtains abundant location information. This can enhance the global understanding of images and solve the issue of insufficient information associated with small targets. Second, the CA attention mechanism is added after each C3 module, whereas the Swin-T attention mechanism module is added before each output layer, increasing the model’s ability to capture multilayer feature information and consequently improving its performance of small target detection. Finally, the accuracy of target localization is ensured using the SIoU penalty function, which considers the target shape and spatial relationships, thereby increasing the model’s ability to capture the positional relationships among targets of different sizes in the image. The STD-YOLOv5s model was validated using the TT100K dataset by ablation and comparison experiments. Experimental results indicate that the proposed model not only maintains the lightweight nature and high detection speed of the YOLOv5s model but also achieves improvements in precision, recall, and average precision.

Keywords

• small target detection
• traffic sign recognition
• attention mechanism
• yolov5s
• deep learning