基于TSCD模型的轨道板裂缝检测方法

为解决轨道板裂缝检测问题,提出了一种基于分支级联卷积神经网络的轨道板裂缝检测模型——TSCD.首先该模型通过注意力机制和搜索分支结构突出轨道板裂缝的位置信息,同时抑制干扰信息;然后采用检测分支结构完成裂缝的像素级检测;最后对检测结果中出现的图像细节退化问题,利用参数映射关系实现特征图的上采样.实验结果表明:所提出的方法...

Track Slab Crack Detection Method Based on TSCD Model

In order to solve the problem of track slab crack detection, a track slab crack detection model based on branch cascaded convolutional neural network, TSCD, is proposed. First, the model highlights the position information of track slab cracks through attention mechanism and structure of search branches to suppress interference information. Second, it realizes the pixel-level detection of cracks by structure of detecting branches. Finally, in order to solve the problem of image detail degradation in detection results, parameter mapping is used to achieve up-sampling of the feature maps. Experimental results show that the proposed model in this paper can not only detect the cracks in track plate surface images accurately with pixel accuracy rate of 97.56% and F1-score of 86.28%, but also performs strong generalization in cross-dataset tests.