摘要
针对混凝土裂缝检测具有多类别影响的复杂性,难以做准确分类、分割和定位任务问题,提出基于改进Mask R-CNN钢纤维混凝土裂缝检测方案。为提高检测速率和精度,对方案模型主干网络增加分散注意力模块跨越特征图组,提高特征学习能力,在交并比基础上增加目标与锚框间距离、重叠率、尺度和惩罚项提高回归精度,并与原始Mask R-CNN模型进行对比。仿真结果表明裂缝、数字以及词汇的平均精度均值达到96.09%,能够精准定位裂缝并作出像素级分割,单样本耗时198 ms。提出的模型既增加了准确率又降低了图片处理延时,与原始Mask R-CNN模型相比,平均精度均值和图片处理速率分别提升6.2%和5.7%。仿真实验证明改进后的模型具有较强的鲁棒性以及泛化能力。
Concrete crack detection is difficult to accurately classify, segment and locate due to the complexity of multiple classification. To solve the above problems, this paper proposes a steel fiber reinforced concrete(SFRC)crack detection model based on improved Mask R-CNN. In order to improve the detection rate and accuracy, a distraction module is added to the backbone network of the scheme model to span the feature graph group and improve the capability of feature learning. Based on intersection of union(IoU), the distance between the target and anchor frame, the overlap rate, the scale and the penalty term were increased to improve the regression accuracy, and compared with the original Mask R-CNN model. The simulation results show that the mean average precision of crack and number classification, segmentation and positioning is 96.09%, the model can accurately locate cracks and make pixel-level segmentation and the single image takes 198 ms. The proposed model increases the accuracy and reduces the image processing delay. Compared with the original Mask R-CNN model,the mean average precision and image processing rate are increased by 6.2% and 5.7% respectively. Experimental results show that the proposed model has strong robustness and generalization ability.
作者
周双喜
袁海强
邓芳明
Zhou Shuangxi;Yuan Haiqiang;Deng Fangming(School of Civil Engineering and Architecture,East China Jiaotong University,Nanchang 330013,China;School of Electrical and Automation Engineering East China Jiaotong University,Nanchang 330013,China)
出处
《华东交通大学学报》
2021年第6期37-45,共9页
Journal of East China Jiaotong University
基金
国家自然科学基金项目(516622008)
江西省自然科学基金项目(20181BA206007)。
