论文标题
使用基于U-NET的卷积神经网络的自动路面裂纹分割
Automated Pavement Crack Segmentation Using U-Net-based Convolutional Neural Network
论文作者
论文摘要
自动化的路面裂纹图像分割由于固有的不规则模式，照明条件和图像中的噪声而具有挑战性。常规方法需要大量的特征工程，以区分裂纹区域和非影响区域。在本文中，我们提出了一种基于卷积神经网络的深度学习技术，以在路面裂纹图像上执行分割任务。与其他机器学习技术相比，我们的方法需要最少的功能工程。我们提出了一个基于U-NET的网络体系结构，在该体系结构中，我们可以用预验证的Resnet-34神经网络代替编码器。我们根据周期性学习率使用“单周”培训时间表来加快收敛速度。我们的方法在CFD数据集上获得了96％的F1分数，而Crack500数据集的F1得分为73％，表现优于在这些数据集上测试的其他算法。我们对各种技术进行消融研究，以帮助我们获得边际性能提升，即增加空间和频道挤压和激发（SCSE）模块，逐渐增加图像尺寸的训练，并培训具有不同学习率的各种神经网络层。
Automated pavement crack image segmentation is challenging because of inherent irregular patterns, lighting conditions, and noise in images. Conventional approaches require a substantial amount of feature engineering to differentiate crack regions from non-affected regions. In this paper, we propose a deep learning technique based on a convolutional neural network to perform segmentation tasks on pavement crack images. Our approach requires minimal feature engineering compared to other machine learning techniques. We propose a U-Net-based network architecture in which we replace the encoder with a pretrained ResNet-34 neural network. We use a "one-cycle" training schedule based on cyclical learning rates to speed up the convergence. Our method achieves an F1 score of 96% on the CFD dataset and 73% on the Crack500 dataset, outperforming other algorithms tested on these datasets. We perform ablation studies on various techniques that helped us get marginal performance boosts, i.e., the addition of spatial and channel squeeze and excitation (SCSE) modules, training with gradually increasing image sizes, and training various neural network layers with different learning rates.
