Efficient attention-based deep encoder and decoder for automatic crack segmentation.

ABSTRACT: Recently, crack segmentation studies have been investigated using deep convolutional neural networks. However, significant deficiencies remain in the preparation of ground truth data, consideration of complex scenes, development of an object-specific network for crack segmentation, and use of an evaluation method, among other issues. In this paper, a novel semantic transformer representation network (STRNet) is developed for crack segmentation at the pixel level in complex scenes in a real-time manner. STRNet is composed of a squeeze and excitation attention-based encoder, a multi head attention-based decoder, coarse upsampling, a focal-Tversky loss function, and a learnable swish activation function to design the network concisely by keeping its fast-processing speed. A method for evaluat

SUBMITTER: Kang DH 

PROVIDER: S-EPMC9411784 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature