改进YOLOv8检测交通标志，提升小目标与复杂环境下的识别准确率。
Enhancing Traffic Sign Recognition On The Performance Based On Yolov8
- 融合坐标注意力与双向特征金字塔，增强小目标感知能力。
- 在GTSRB等数据集上准确率显著提升，边缘设备实时推理性能良好。
- 适合自动驾驶与高级驾驶辅助系统研发人员参考使用。
交通标志识别在自动驾驶与高级驾驶辅助系统发展中至关重要。尽管深度学习与目标检测技术取得进展，但因标志尺寸小、环境变化大、遮挡及类别不平衡等问题，精准检测仍具挑战。本文提出一种增强型YOLOv8检测系统，整合先进数据增强、新型架构改进（如坐标注意力CA、双向特征金字塔BiFPN）以及动态模块（ODConv、LSKA），并采用改进损失函数（EIoU与WIoU结合焦点损失）。在GTSRB、TT100K和GTSDB数据集上的大量实验表明，系统在检测精度、恶劣条件鲁棒性及边缘设备实时推理方面均有显著提升。研究成果为真实场景下部署可靠交通标志识别系统提供了实用洞见。
原文摘要 · Abstract (English)
This paper Traffic sign recognition plays a crucial role in the development of autonomous vehicles and advanced driver-assistance systems (ADAS). Despite significant advances in deep learning and object detection, accurately detecting and classifying traffic signs remains challenging due to their small sizes, variable environmental conditions, occlusion, and class imbalance. This thesis presents an enhanced YOLOv8-based detection system that integrates advanced data augmentation techniques, novel architectural enhancements including Coordinate Attention (CA), Bidirectional Feature Pyramid Network (BiFPN), and dynamic modules such as ODConv and LSKA, along with refined loss functions (EIoU and WIoU combined with Focal Loss). Extensive experiments conducted on datasets including GTSRB, TT100K, and GTSDB demonstrate marked improvements in detection accuracy, robustness under adverse conditions, and real-time inference on edge devices. The findings contribute actionable insights for deploying reliable traffic sign recognition systems in real-world autonomous driving scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。