基于Yolov8s的交通标志检测方法改进

Improvement of Traffic Sign Detection Method Based on Yolov8s

摘要: 在交通标志检测任务中，交通标志的准确识别对于自动驾驶和智能交通系统至关重要。本文对Yolov8s模型改进并在CCTSDB2021数据集中进行实验以评估模型的性能，我们采取了以下改进，引入了专门针对小目标设计的检测头，该检测头通过优化特征图的尺度，增强了模型对小尺寸交通标志的识别能力。采用了SIoU损失函数，有助于提升小目标的定位精度。使用模块Star Block集成了一个轻量化模块LB，该模块通过减少参数量同时保持模型检测能力。在CCTSDB2021数据集上的实验结果表明，经过以上改进的Yolov8s模型在检测小尺寸交通标志时，不仅提高了检测准确率，而且减少了一定的参数量，展现了模型改进的有效性。

Abstract: In the traffic sign detection task, accurate recognition of traffic signs is crucial for autonomous driving and intelligent transportation systems. In this paper, we improve the Yolov8s model and conduct experiments in the CCTSDB2021 dataset to evaluate the performance of the model. We have made the following improvements: introducing a detection head specifically designed for small targets. This detection head enhances the model’s recognition ability for small-sized traffic signs by optimizing the scale of the feature map. The SIoU loss function is adopted, which helps to improve the positioning accuracy of small targets. Using the module Star Block, we integrated a lightweight module LB. This module reduces the number of parameters while maintaining the model’s detection ability. The experimental results on the CCTSDB2021 dataset show that the improved Yolov8s model not only improves the detection accuracy but also reduces a certain number of parameters when detecting small-sized traffic signs, demonstrating the effectiveness of model improvement.