Grasping detection method of irregular shaped parts based on deep learning

被引：0

作者：

Sun, Xiantao ^{[1
]}

Yang, Yinming ^{[1
]}

Wang, Chen ^{[1
]}

Chen, Wenjie ^{[1
]}

Hu, Xiangtao ^{[1
]}

Chen, Weihai ^{[2
]}

机构：

[1] School of Electrical Engineering and Automation, Anhui University, Hefei,230601, China

[2] School of Automation Science and Electrical Engineering, Beihang University, Beijing,100191, China

来源：

Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2025年 / 31卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Deep neural networks - Palmprint recognition - Robotics;

D O I：

10.13196/j.cims.2022.0615

中图分类号：

学科分类号：

摘要：

The problems that the Visual System cannot accurately locate parts due to the defects of machined parts have seriously affected the promotion of production automation in small and medium-sized enterprises. To solve this Problem, a grasping detection method for abnormal parts was proposed. A Key Point Detection Model (KPDM) based on deep learning was designed to detect the grasping key points of different parts, and then a pose solving module was designed according to the key point position Information and hand-eye calibration parameters to calculate the grasping pose of the parts. By combining the architecture of the image segmentation model Deeplab V3+ with the heatmap supervision method, KPDM could capture keypoints from input images. The experimental results showed that the proposed Visual grasping System could accurately estimate the position and orientation of parts with different shapes. Taking the electric iron soleplates as examples, the detection success rates for complete and incom-plete soleplates in different lighting environments were 97. 2% and 92. 7% respectively. © 2025 CIMS. All rights reserved.

引用

页码：490 / 498