The advancement of Image Processing has led to the widespread use of Object Recognition (OR) models in various applications, such as airport security and mail sorting. These models have become essential in signifying the capabilities of AI and supporting vital services like national postal operations. However, the performance of OR models can be impeded by real-life scenarios, such as traffic sign alteration. Therefore, this research investigates the effects of altered traffic signs on the accuracy and performance of object recognition models. To this end, a publicly available dataset was used to create different types of traffic sign alterations, including changes to size, shape, color, visibility, and angles. The impact of these alterations on the YOLOv7 (You Only Look Once) model's detection and classification abilities were analyzed. It reveals that the accuracy of object detection models decreases significantly when exposed to modified traffic signs under unlikely conditions. This study highlights the significance of enhancing the robustness of object detection models in real-life scenarios and the need for further investigation in this area to improve their accuracy and reliability.

翻译：图像处理技术的进步推动了目标识别模型在机场安检、邮件分拣等各类应用中的广泛部署。这些模型已成为彰显人工智能能力的重要载体，并支撑着国家邮政业务等关键服务。然而，现实场景中的交通标志改动等实际因素可能阻碍目标识别模型的性能。因此，本研究探究了交通标志改动对目标识别模型准确率与性能的影响。为此，我们基于公开数据集构建了不同类型交通标志的改动方案，涵盖尺寸、形状、颜色、可见度及角度变化。重点分析了这些改动对YOLOv7目标检测与分类能力的影响。结果表明，当模型在非典型条件下处理被修改的交通标志时，其检测准确率显著下降。本研究凸显了提升目标检测模型在真实场景中鲁棒性的重要意义，并指出了该领域需进一步探索以改善模型准确性与可靠性的必要性。