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.