Efficient and Lightweight Spatial Attention-Based Shallow CNNs with Superior Performance for Concrete Surface Crack Detection

Abstract

To solve the problems of high training cost and low time efficiency when detecting concrete surface crack defects, this paper proposes a concrete surface crack detection method based on shallow CNNs. First, an image dataset of concrete surface cracks is constructed and preprocessing operations are performed on the dataset. Then, a shallow CNNs model for concrete surface crack detection is constructed, hyperparameters of the network model are set, spatial attention mechanism is introduced, and the dataset is input to the model for training. Finally, the results are analyzed and evaluated, and compared with the mainstream deep learning models InceptionV3 and Resnet50. Experimental results demonstrate that our proposed method achieves a 99.56% accuracy rate for detecting cracks on concrete surfaces, while also offering higher time efficiency and lower training costs. Given the significantly higher training costs associated with mainstream deep learning models such as InceptionV3 and ResNet50, our proposed method holds promise for future applications in industrial production processes and the intelligent field of construction quality inspection. By effectively, automatically, and accurately identifying cracks on concrete surfaces, it can reduce the risk of catastrophic failures.