Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation

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 …

conflict convolution cs.cv deep learning detection guidance location network networks neural network neural networks objects performance real-time role segmentation semantic work