A lightweight convolutional neural network for automated crack inspection

Abstract

Convolutional neural networks (CNN) have been increasingly embraced in building inspection and maintenance to realize automated crack inspection. However, CNNs are still less frequently utilized in the construction industry because most of them are designed with complex architectures that need to be executed by high-powered and expensive GPUs. To improve its practical applicability, this study aims to design a CNN model with a lightweight architecture for crack inspection. The lightweight design is expected to be feasible on a variety of electronic devices, such as mobile phones. To do so, layer pruning and parameter reduction methods were employed to design model depth and weight, respectively. Specifically, the CNN depth was first determined by training and testing 24 experimental groups that contain sequentially increased convolutional and pooling blocks. Secondly, 4 × 4 convolutional filters with 2 strides were inserted between adjacent convolutional layers to reduce model weight and memory. As a result, only 6.82 million weights and 24 M of memory were remined, compared to 138 million weights and 421 M of memory in the widely utilized model VGG-16. During an on-site validation, the proposed lightweight CNN was successfully executed on a mobile robot powered by a CPU-driven robotic control board. Both cracked and non-cracked surfaces could be detected automatically, remotely, and in real-time. Outstanding performance was also demonstrated, with F1-scores in the training and testing datasets of 96.8% and 92.4%, respectively. Overall, the designed lightweight CNN model is validated as more practicable for on-site inspection devices, which facilitate the wide spread of computer vision-based technologies in the construction industry. Meanwhile, the findings provide a potential solution for balancing the weight and accuracy of CNNs.