Enhanced YOLO Network for Improving the Efficiency of Traffic Sign Detection

Abstract

One important task for autonomous driving is the precise detection and recognition of road traffic signs. This research focuses on a comprehensive set of 72 distinct traffic signs that are prevalent on urban roads in China, with the goal of developing an enhanced You Only Look Once (YOLO) network model tailored for this specific task. The modifications include the omission of the terminal convolution module and Conv3 (C3) module within the backbone network. Additionally, the 32-fold downsampling is replaced with a 16-fold downsampling, and a feature fusion module with dimensions of 152 × 152 is introduced in the feature layer. To capture a more encompassing context, a novel hybrid space pyramid pooling module, referred to as Hybrid Spatial Pyramid Pooling Fast (H-SPPF), is introduced. Furthermore, a channel attention mechanism is integrated into the framework, combined with three other improved methodologies. Upon evaluation, the enhanced algorithm demonstrates impressive results, achieving a precision rate of 91.72%, a recall rate of 91.77%, and a mean average precision (mAP) of 93.88% at an intersection over union (IoU) threshold of 0.5. Additionally, the method also achieves an mAP of 75.81% for a variety of IoU criteria between 0.5 and 0.95. These achievements are validated on an augmented dataset established for this study.

Keywords

• traffic sign detection
• feature fusion
• object detection