Embedded surface crack image classification based on multi-model voting

The safety of concrete structures is crucial for the sustainable development of infrastructure. However, accurate detection of concrete surface cracks has always been a challenging task. With the continuous development of deep learning technology, image-based concrete crack detection has made significant progress in the field of automation. However, running efficient deep learning models on embedded devices remains challenging due to their limited computational resources and storage capacity. To address this challenge, a surface crack image classification method based on multi-model voting is proposed in this paper to enhance real-time performance on embedded devices. In this study, three classical deep learning models were selected: InceptionResNetV2, VGG16, and Xception. By combining these models, we leverage their unique advantages in image feature extraction, constructing a multi-model fusion classification system. During the classification process, a voting mechanism was employed, ensuring the reliability of the final classification results. Experimental results demonstrate that compared to single-model approaches, our multi-model method achieves significant improvements in both classification accuracy. The contribution of this research is not only reflected in technical innovation but also provides a feasible solution to the practical problem of automated concrete surface crack detection.