Intelligent rebar inspection based on improved Mask R-CNN and stereo vision
A rebar inspection method based on improved mask region with convolutional neural network(Mask R-CNN)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 R-CNN+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.
NETL
NSTL
万方数据
维普
