CycleADC-Net: A crack segmentation method based on multi-scale feature fusion

Crack detection is an important factor in structural safety assessment. But there are many challenges in detecting crack, especially for the ones with very thin shape, uneven intensity, or lying in a complex background. In addition, the crack image captured in poor lighting conditions makes it more difficult to be detected. Most of the existing crack detection methods are designed for well-light cracks, whose detection performance drops dramatically on low-light crack detection. To overcome these challenges, a novel encoder-decoder segmentation network, called CycleADC-Net is proposed in this work which opens a new idea to detect the crack images in low-light condition. A cycle generative adversarial network is employed to translate the low-light crack image to the bright domain, and its output is fed to the follow-up encoder-decoder segmentation network for final detection. The dual-channel feature extraction module and the attention mechanism are both introduced to extract semantic multi-scale image features, reduce information loss and suppress irrelevant background information when performing crack segmentation. The experimental results show that the proposed CycleADC-Net performs superior both on low-light crack or well-light crack databases over recent segmentation networks, suggesting it has good generalization ability and great potential in mad inspection task under poor lighting environment.

Automated summary

This paper proposes a novel segmentation network for crack image detection under low-light conditions. A cycle generative adversarial network is used to translate low-light images to the bright domain, and an encoder-decoder segmentation network is employed for final crack detection. Experimental results show that the proposed network performs superiorly in both low-light and well-light conditions, indicating its potential for inspection tasks in poor lighting environments.