School of Highway, Chang'an University, Xi'an 710064, Shaanxi, China.
Inner Mongolia Transport Construction Engineering Quality Supervision Bureau, Hohhot 010020, Inner Mongolia, China.
Sensors (Basel). 2021 Jan 26;21(3):824. doi: 10.3390/s21030824.
Cracks and exposed steel bars are the main factors that affect the service life of bridges. It is necessary to detect the surface damage during regular bridge inspections. Due to the complex structure of bridges, automatically detecting bridge damage is a challenging task. In the field of crack classification and segmentation, convolutional neural networks have offer advantages, but ordinary networks cannot completely solve the environmental impact problems in reality. To further overcome these problems, in this paper a new algorithm to detect surface damage called EMA-DenseNet is proposed. The main contribution of this article is to redesign the structure of the densely connected convolutional networks (DenseNet) and add the expected maximum attention (EMA) module after the last pooling layer. The EMA module is obviously helpful to the bridge damage feature extraction. Besides, we use a new loss function which considers the connectivity of pixels, it has been proved to be effective in reducing the break point of fracture prediction and improving the accuracy. To train and test the model, we captured many images from multiple bridges located in Zhejiang (China), and then built a dataset of bridge damage images. First, experiments were carried out on an open concrete crack dataset. The mean pixel accuracy (MPA), mean intersection over union (MIoU), precision and frames per second (FPS) of the EMA-DenseNet are 87.42%, 92.59%, 81.97% and 25.4, respectively. Then we also conducted experiments on a more challenging bridge damage dataset, the MIoU, where MPA, precision and FPS were 79.87%, 86.35%, 74.70% and 14.6, respectively. Compared with the current state-of-the-art algorithms, the proposed algorithm is more accurate and robust in bridge damage detection.
裂缝和暴露的钢筋是影响桥梁使用寿命的主要因素。有必要在定期的桥梁检查中检测表面损伤。由于桥梁结构复杂，自动检测桥梁损伤是一项具有挑战性的任务。在裂缝分类和分割领域，卷积神经网络具有优势，但普通网络无法完全解决现实中的环境影响问题。为了进一步克服这些问题，本文提出了一种新的表面损伤检测算法，称为 EMA-DenseNet。本文的主要贡献是重新设计密集连接卷积网络（DenseNet）的结构，并在最后一个池化层后添加预期最大注意力（EMA）模块。EMA 模块对桥梁损伤特征提取有明显的帮助。此外，我们使用了一种新的损失函数，该函数考虑了像素的连通性，已被证明在减少断裂预测的断点和提高精度方面非常有效。为了训练和测试模型，我们从位于中国浙江的多座桥梁上拍摄了许多图像，然后构建了一个桥梁损伤图像数据集。首先，我们在一个开放的混凝土裂缝数据集上进行了实验。EMA-DenseNet 的平均像素准确率（MPA）、平均交并比（MIoU）、精度和每秒帧数（FPS）分别为 87.42%、92.59%、81.97%和 25.4。然后，我们还在更具挑战性的桥梁损伤数据集上进行了实验，MPA、精度和 FPS 分别为 79.87%、86.35%、74.70%和 14.6。与当前最先进的算法相比，所提出的算法在桥梁损伤检测中更准确、更稳健。
