Localization Method of Rebar Tying Nodes Based on Binocular Vision

Aiming at the problems at an actual rebar binding construction site, such as multilayered rebar mesh, complex operating environments and light, and dense components, and to realize the accurate positioning of rebar binding nodes, a joint localization method for rebar binding nodes based on binocular stereoscopic matching and binding state recognition is proposed starting from the actual needs of multilayer rebar skeleton plane binding. This method is based on joint target recognition with binocular vision. First, the feature extraction network of AnyNet is improved by introducing a Hourglass feature extraction network and an Efficient Channel Attention Network (ECANet) to improve matching accuracy in the rebar mesh region. As the multilayer rebar mesh has a complex structure and interlayer relationship, the target lashing work layer is obtained by depth filtering. Second, a lashing node localization model based on rebar skeleton extraction is proposed according to the characteristics of the target lashing work. Additionally, the coordinates of the rebar lashing nodes are obtained by extracting the rebar skeleton and fitting the equation of the rebar skeleton. Finally, the state of lashing nodes is identified by the light-weighted YOLOv5 to output the coordinates of the points to be tied. The experimental results show that the three Pixel Error (3PE) of the benchmark network AnyNet is 8.16% and that of the proposed algorithm is only 3.72%, which effectively improves the matching accuracy of the algorithm. The proposed algorithm can filter out the interference of deep-seated rebar, and the average error of the spatial localization of rebar tying nodes is 5.03 mm, which can satisfy the demand of rebar tying in a complex background.