College of Information and Control Engineering, Xi’an University of Architecture and Technology
混凝土内部损伤破坏形态具有明显的离散性和随机性,内部损伤特征检测是混凝土细观研究的重要内容,针对已有混凝土结构内部损伤特征检测模型精度低的问题,本文提出一种特征共享双头Cascade R-CNN模型对混凝土CT图像的损伤特征进行检测。首先为了有效识别损伤特征的空间信息,构建具有空间敏感性的fc-head(fully connected head)和空间相关性的conv-head(convolution head)相结合的Cascade R-CNN网络模型。其次通过特征共享的方法将检测网络各层级分类信息进行融合,提升低IOU(Intersection over Union)阈值(0.5-0.7)ROI(Regions of Interest)检测任务的精度。实验结果表明,所提方法在检测混凝土CT图像的损伤特征中平均精度达到91.31%,比原始的Cascade R-CNN提高3.04%,低IOU阈值(0.5-0.7)ROI平均精度提高1.49%。该模型可以较好地从混凝土CT图像中检测出细观损伤部分,具有精度高、运算简单、易于工程实现等特点。
The internal damage of concrete has obvious characteristics of discreteness and randomness, the detection of internal damage characteristics is an important content of concrete mesoscopic research. To solve the problem of low precision of existing models, this paper proposes a Double-head Cascade R-CNN model with feature sharing to detect the damage features of concrete CT images. Firstly, a Cascade R-CNN network model, which combines spatially sensitive fc-head (fully connected head) and spatially correlation conv-head (convolution head), is constructed to effectively identify the spatial information of the damage feature. Next, the classification information of each level of the detection network is merged through the feature sharing method, which improved the precision of low IOU (Intersection over Union) threshold (0.5-0.7) ROI (Regions of Interest) detection tasks. The results show that the AP (average precision) of the proposed method is 91.31%, which is 3.04% higher than that of the original. The average accuracy of low IOU threshold (0.5-0.7) ROI is improved by 1.49%. It can better detect the mesoscopic damage part from the concrete CT image with the features of high precision, simple computation and easy engineering realization.