基于改进Mask R-CNN与双目视觉的智能配筋检测

Intelligent rebar inspection based on improved Mask R-CNN and stereo vision

为了提高配筋检测的智能化水平,提出基于改进掩膜区域卷积神经网络(Mask R-CNN)模型与双目视觉技术的配筋检测方法.通过在Mask R-CNN中加入自下而上的注意力机制路径,形成了带通道注意力和空间注意力的掩膜区域卷积神经网络(Mask R-CNN+CA-SA)改进模型.结合双目视觉技术进行坐标转换,获取钢筋直径与间距,实现智能配筋检测.在自建的包含3 450张钢筋图片的数据集上进行训练,结果表明,改进模型的F1分数和全类平均精确率(mAP)相较于Mask R-CNN基础网络分别提高了2.54%和2.47%.通过钢筋网验证试验和复杂背景测试,钢筋直径的绝对误差和相对误差基本小于1.7 mm和10%,钢筋间距的绝对误差和相对误差分别小于4mm和3.2%,所提方法在实际应用中具有较强的可操作性.智能配筋检测技术在保证足够的检测精度的同时,能够大大提升工效,降低人工成本.

A rebar inspection method based on improved mask region with convolutional neural network(Mask RCNN)model and stereo vision technology was proposed in order to promote the transformation of reinforcement inspection to intelligence.The improved model Mask R-CNN with channel attention and spatial attention(Mask RCNN+CA-SA)was formed by adding a bottom-up path with attention mechanism in Mask R-CNN.The diameter and spacing of rebar can be obtained by combining stereo vision technology for coordinate transformation,thereby achieving intelligent rebar inspection.The training was conducted on a self-built dataset containing 3450 rebar pictures.Results showed that the Mask R-CNN+CA-SA model increased the F1 score and mean average precision(mAP)by 2.54%and 2.47%compared with the basic network of Mask R-CNN,respectively.The rebar mesh verification test and complex background test showed that the absolute error and relative error of rebar diameter were basically controlled within 1.7 mm and 10%,and the absolute error and relative error of rebar spacing were controlled within 4 mm and 3.2%respectively.The proposed method is highly operable in practical applications.The intelligent rebar inspection technology can greatly improve work efficiency and reduce labor costs while ensuring sufficient inspection accuracy.