Guidance Method of Binocular Vision Unloading Robot Based on Improved Mask R-CNN
XU Zhixiang
ZHAO Yan
XING Lidong
GAO Dong
Abstract：Aiming at the problems of stacking and positioning of unloading targets,the target guidance system is designed ac-cording to the existing rectangular coordinate system of unloading robots.Based on the in-depth learning target detection model Mask R-CNN,an improvement of the fusion CBAM attention mechanism is proposed according to the feature pyramid FPN,which optimizes the target area and channel weight,and completes the target cargo identification based on the fusion features.The 3D in-formation of the input image is calculated by SGBM binocular stereo vision algorithm,and the 3D coordinates of the target are ex-tracted.The experimental results show that the average precision of the proposed method for cargo target recognition is 90.20%,and the maximum error of the depth direction positioning accuracy is not more than 15 mm.The method meets the guidance requirements of the unloading robot for stacking cargo.
Keywords：unloading robotdeep learningMask R-CNNCBAM attention mechanismSGBM
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：6( 3287-3292 )