Deep learning has been widely applied to vision-based structural damage detection, but its computational demand is high. To avoid this computational burden, a novel crack detection system, namely, fusion features-based broad learning system (FF-BLS), is proposed for efficient training without GPU acceleration. In FF-BLS, a convolution module with fixed weights is used to extract the fusion features of images. Feature nodes and enhancement nodes randomly generated by fusion features are used to estimate the output of the network. Meanwhile, the proposed FF-BLS is a dynamical system, which achieves incremental learning by adding nodes. Thus, the trained FF-BLS model can be updated efficiently with additional data, and this substantially reduces the training cost. Finally, FF-BLS was applied to crack detection. Compared with some well-known deep convolutional neural networks (VGG16, ResNet50, InceptionV3, Xception, and EfficientNet), the FF-BLS achieved a similar level of recognition accuracy, but the training speed was increased by more than 20 times.

中文翻译：

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使用基于融合特征的广泛学习系统和图像处理进行裂纹检测

深度学习已广泛应用于基于视觉的结构损伤检测，但其计算需求较高。为了避免这种计算负担，提出了一种新颖的裂纹检测系统，即基于融合特征的广泛学习系统（FF-BLS），用于在没有 GPU 加速的情况下进行高效训练。在FF-BLS中，使用具有固定权重的卷积模块来提取图像的融合特征。融合特征随机生成的特征节点和增强节点用于估计网络的输出。同时，所提出的 FF-BLS 是一个动态系统，通过添加节点实现增量学习。因此，经过训练的 FF-BLS 模型可以使用额外的数据进行有效更新，这大大降低了训练成本。最后，将FF-BLS应用于裂纹检测。