Efficient rebar bundling: vision robotics innovations

Abstract

The application of vision robots in rebar bundling operations has aroused great interest in the industry. The performance cost and complex application environment have become the key constraints to its development. Based on this, in this paper, we firstly design a rebar bundling intersection image acquisition system for rebar intersections in complex environments; secondly, we use GhostNet to lighten the backbone network and combine the Convolutional Block Attention Module (CBAM) and Bidirectional Feature Pyramid Network (BiFPN) with the neck to improve the adaptive ability to the complex environments; and finally, in order to make our network has better robustness, we try to introduce Distance Intersection Union (DIoU) in the network and verify the performance of the improved model through comparison and ablation experiments. The experimental results show that the mAP, accuracy and recall of the improved YOLOv5 model are 97.8%, 95.6% and 95.4%, which are 1.1%, 0.9% and 0.1% higher than that of YOLOv5s, respectively. Meanwhile, computational quantization and parameter quantization are reduced by 36% and 34%, respectively. This indicates that our proposed improved network has better intersection detection performance and also provides favorable conditions for deployment in robotic systems.