MobileNetV3-BLS: A broad learning approach for automatic concrete surface crack detection

• Published in: Construction & building materials 2023-08, Vol.392, p.131941, Article 131941
• Main Authors: Zhang, Jing, Cai, Yuan-Yuan, Yang, Dong, Yuan, Ye, He, Wen-Yu, Wang, Yan-Jia
• Format: Article
• Language: English
• Subjects: Broad learning system; Crack detection; Deep convolution neural network; Incremental learning
• ISSN: 0950-0618; 1879-0526
• DOI: 10.1016/j.conbuildmat.2023.131941

•A lightweight and efficient BLS-based crack detection model is proposed to automatically classify and identify crack images.•The bottleneck structure of MobileNetV3 is introduced as the convolution module of the proposed broad learning system for feature extraction.•By training and validating with public and realistic crack image datasets, the proposed model achieves fast and efficient incremental learning as training samples increase.•The sliding window algorithm is introduced to efficiently recognize and locate cracks in the large-size image. Concrete surface crack detection is an important means to ensure infrastructure security, including bridges and tunnels. In recent years, computer vision has been widely adopted for concrete surface crack detection, and the intelligent crack detection method based on computer vision has achieved good results with the breakthrough of the deep learning algorithm. However, there are also some challenges, such as complex architecture, high computing burden, and time-consuming training. In this paper, a lightweight broad learning system detection method called MobileNetV3-BLS is proposed. First, the inverted residual structure with the linear bottleneck of MobileNetV3 is introduced as the convolution module of the proposed broad learning system for feature extraction. Then the extracted features are employed to randomly generate both mapping and enhancement nodes in a broad learning system, which are used to train the crack detection model. The proposed MobileNetV3-BLS can improve crack detection accuracy and training speed compared with other classical deep learning networks. On the other hand, the proposed method is a dynamic network of which the weights can be updated quickly to achieve incremental learning by adding new nodes and samples.