The development of Convolutional Neural Networks (CNN) has expanded with the accelerated progress of IT, as well as with the needs of the autonomous vehicle (AV) implementation. The specifics and requirements of AV towards the infrastructure primarily relate to the condition and quality of traffic signs. For the independent participation of these vehicles in traffic, an impeccable traffic sign condition is required, which is often not the case in practice. Damaged, faded, obscured, or vandalized traffic signs can usually be seen in the road network, which can impede the movement of AV in traffic. In the existing literature, little or very little attention is focused on the problem of identifying and classifying damaged and especially vandalized traffic signs. In this paper, the mentioned problem is addressed, and the CNN model is proposed. This model has been tested on a specially designed novel and challenging database containing 6,000 real-time images of traffic signs in the road network of the Republic of Serbia. This model is invariant to different lighting and weather (nighttime and fog) conditions. In this case study, the model reached an overall accuracy of 99.17%, whereby all vandalized and damaged traffic signs are accurately identified and classified.

随着 IT 的加速进步以及自动驾驶汽车 (AV) 实施的需求，卷积神经网络 (CNN) 的发展不断扩大。自动驾驶对基础设施的具体要求和要求主要与交通标志的状况和质量有关。这些车辆要独立参与交通，需要具备无可挑剔的交通标志条件，而实际情况往往并非如此。道路网络中通常可以看到损坏、褪色、模糊或被破坏的交通标志，这会阻碍自动驾驶汽车在交通中的移动。在现有文献中，很少或很少关注识别和分类损坏的特别是被破坏的交通标志的问题。本文针对上述问题提出了CNN模型。该模型已经在一个专门设计的新颖且具有挑战性的数据库上进行了测试，该数据库包含 6,000 幅塞尔维亚共和国道路网络中交通标志的实时图像。该模型对于不同的光照和天气（夜间和雾）条件是不变的。在本案例研究中，该模型的总体准确率达到了 99.17%，从而准确识别和分类了所有被破坏和损坏的交通标志。