An Enhanced Algorithm for Detecting Small Traffic Signs Using YOLOv10

Recognizing traffic signs is crucial for autonomous driving systems, as it significantly impacts their safety and dependability. However, challenges like the diminutive size of objects and intricate background environments limit the effectiveness of current object detection models. To improve small traffic sign detection, this research introduces an enhanced detection algorithm built on YOLOv10. First, a custom-designed layer for detecting small objects is integrated into the neck section of the network, enhancing the feature extraction process for these objects. Second, a refined downsampling module, called Triple-Branch Downsampling (TBD), utilizes a multi-branch structure and hybrid pooling strategy to boost feature extraction efficiency within the model. Finally, the loss function is optimized by integrating the Normalized Wasserstein Distance (NWD) and Wise-MPDIoU mechanisms, increasing the accuracy of bounding box matching and regression. The experimental findings indicate that the enhanced algorithm reaches a [email protected] of 84.8%, marking a 4% increase over YOLOv10. The classification accuracy and recall are 73.4% and 82.9%, respectively. Moreover, the parameter count decreases by approximately 10%, while the computational complexity is reduced by around 5%.