Autonomous Identification of Bridge Concrete Cracks Using Unmanned Aircraft Images and Improved Lightweight Deep Convolutional Networks

The identification of the development of structural defects is an important part of bridge structure damage diagnosis, and cracks are considered the most typical and highly dangerous structural disease. However, existing deep learning-based methods are mostly aimed at the scene of concrete cracks, while they rarely focus on designing network architectures to improve the vision-based model performance from the perspective of unmanned aircraft system (UAS) inspection, which leads to a lack of specificity. Because of this, this study proposes a novel lightweight deep convolutional neural network-based crack pixel-level segmentation network for UAS-based inspection scenes. Firstly, the classical encoder-decoder architecture UNET is utilized as the base model for bridge structural crack identification, and the hourglass-shaped depthwise separable convolution is introduced to replace the traditional convolutional operation in the UNET model to reduce model parameters. Then, a kind of lightweight and efficient channel attention module is used to improve model feature fuzzy ability and segmentation accuracy. We conducted a series of experiments on bridge structural crack detection tasks by utilizing a long-span bridge as the research item. The experimental results show that the constructed method achieves an effective balance between reasoning accuracy and efficiency with the value of 97.62% precision, 97.23% recall, 97.42% accuracy, and 93.25% IOU on the bridge concrete crack datasets, which are significantly higher than those of other state-of-the-art baseline methods. It can be inferred that the application of hourglass-shaped depth-separable volumes can actively reduce basic model parameters. Moreover, the lightweight and efficient attention modules can achieve local cross-channel interaction without dimensionality reduction and improve the network segmentation performance.