Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation
This work addresses the problem of real-time, detailed crack segmentation for edge device applications in infrastructure inspection, representing an incremental improvement over existing methods.
The paper tackles the trade-off between high-resolution detail and real-time speed in crack segmentation by introducing HrSegNet, a neural network with semantic guidance, which achieves state-of-the-art performance and efficiency on datasets like CrackSeg9k, Asphalt3k, and Concrete3k.
Deep learning plays an important role in crack segmentation, but most work utilize off-the-shelf or improved models that have not been specifically developed for this task. High-resolution convolution neural networks that are sensitive to objects' location and detail help improve the performance of crack segmentation, yet conflict with real-time detection. This paper describes HrSegNet, a high-resolution network with semantic guidance specifically designed for crack segmentation, which guarantees real-time inference speed while preserving crack details. After evaluation on the composite dataset CrackSeg9k and the scenario-specific datasets Asphalt3k and Concrete3k, HrSegNet obtains state-of-the-art segmentation performance and efficiencies that far exceed those of the compared models. This approach demonstrates that there is a trade-off between high-resolution modeling and real-time detection, which fosters the use of edge devices to analyze cracks in real-world applications.