发表机构
K. N. Toosi University of Technology（K. N. Toosi 科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对自动驾驶中的交通标志识别，提出基于分支YOLOv2与几何特征的系统，实现检测分类并降低计算量，实验表明几何验证提升mAP至0.713。
AI 中文摘要
交通标志识别（TSR）是自动驾驶和高级驾驶辅助系统中的一项重要感知任务，系统必须高效地定位交通标志并确定其语义类别。本工作提出了一种基于YOLOv2的TSR系统，用于同时进行检测和分类。研究了两种互补的改进方法。首先，对YOLOv2进行扩展，加入中间预测层，形成分支架构，该架构可以对简单情况提前终止推理并减少计算时间。研究了整幅图像和逐单元两种分支策略。其次，引入几何信息以减少视觉相似标志之间的分类错误。一种无监督的贝叶斯图像分割方法产生二值表示，并与YOLOv2边界框内的类别特定几何模板进行比较。该信息在推理期间或作为训练期间的附加信号使用。通过使用无缝克隆和受控图像变换组合GTSDB和GTSRB样本，构建了一个专用数据集。实验涵盖十种交通标志类别，包含3,000个训练样本和300个测试样本。所选分支架构报告运行时间为0.647秒，mAP为0.680，而基线YOLOv2为0.6607秒和0.680 mAP。推理期间的几何验证将mAP提高到0.713，而几何特征训练变体实现了0.697 mAP，报告运行时间为0.6608秒。
英文摘要
Traffic sign recognition (TSR) is an important perception task for autonomous driving and advanced driver-assistance systems, where a system must both localize traffic signs and determine their semantic classes efficiently. This work presents a TSR system based on YOLOv2 for simultaneous detection and classification. Two complementary modifications are studied. First, YOLOv2 is extended with intermediate prediction layers, forming a branched architecture that can terminate inference early for easy cases and reduce computation time. Both whole-image and cell-wise branching strategies are investigated. Second, geometric information is introduced to reduce classification errors between visually similar signs. An unsupervised Bayesian image-segmentation method produces binary representations that are compared with class-specific geometric templates inside YOLOv2 bounding boxes. This information is used either during inference or as an additional signal during training. A dedicated dataset is constructed by combining GTSDB and GTSRB samples using seamless cloning and controlled image transformations. Experiments cover ten traffic-sign classes, with 3,000 training and 300 test samples. The selected branched architecture reports 0.647 s runtime and 0.680 mAP, compared with 0.6607 s and 0.680 mAP for baseline YOLOv2. Geometric verification during inference increases mAP to 0.713, while the geometric-feature training variant achieves 0.697 mAP with a reported runtime of 0.6608 s.
