Structures suffer from the emergence of cracks, therefore, crack detection is always an issue with much concern in structural health monitoring. Along with the rapid progress of deep learning technology, image semantic segmentation, an active research field, offers another solution, which is more effective and intelligent, to crack detection Through numerous artificial neural networks have been developed to address the preceding issue, corresponding explorations are never stopped improving the quality of crack detection. This paper presents a novel artificial neural network architecture named Full Attention U-net for image semantic segmentation. The proposed architecture leverages the U-net as the backbone and adopts the Full Attention Strategy, which is a synthesis of the attention mechanism and the outputs from each encoding layer in skip connection. Subject to the hardware in training, the experiments are composed of verification and validation. In verification, 4 networks including U-net, Attention U-net, Advanced Attention U-net, and Full Attention U-net are tested through cell images for a competitive study. With respect to mean intersection-over-unions and clarity of edge identification, the Full Attention U-net performs best in verification, and is hence applied for crack semantic segmentation in validation to demonstrate its effectiveness.
翻译:由于出现了裂缝,因此,裂缝检测总是一个在结构健康监测中引起极大关注的问题。随着深层学习技术的迅速进步,图像语义分割(一个活跃的研究领域)提供了另一个更有效和智能的解决方案,以破碎检测。通过开发了无数人工神经网络来解决上述问题,相应的探索从未停止过,因而也从未停止过提高裂缝检测质量。本文展示了一个新的人工神经网络结构,名为“完全注意U-net”,用于图像语义分割。拟议建筑利用U-net作为主干线,并通过了“充分注意战略”,这是关注机制和每个编码层输出结果的结合。在培训中,实验由硬件组成,核查和验证。在核查中,包括U-net、注意U-net、高级注意U-net和充分注意U-net在内的4个网络通过细胞图像进行测试,以进行竞争性研究。关于图像交叉连接和边缘识别的平均值,U-充分注意U-net在核查中表现得最佳,因此用于裂缝断分割的有效性。