Volume 20, Issue 4 (December (Special Issue on ADLEEE) 2024)                   IJEEE 2024, 20(4): 134-146 | Back to browse issues page

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Merrikhi H, Ebrahimnezhad H. Grasp Area Detection for 3D Object using Enhanced Dynamic Graph Convolutional Neural Network. IJEEE 2024; 20 (4) :134-146
URL: http://ijeee.iust.ac.ir/article-1-3472-en.html
Grasp Area Detection for 3D Object using Enhanced Dynamic Graph Convolutional Neural Network
Haniye Merrikhi , Hossein Ebrahimnezhad
Abstract:   (181 Views)
Robots have become integral to modern society, taking over both complex and routine human tasks. Recent advancements in depth camera technology have propelled computer vision-based robotics into a prominent field of research. Many robotic tasks—such as picking up, carrying, and utilizing tools or objects—begin with an initial grasping step. Vision-based grasping requires the precise identification of grasp locations on objects, making the segmentation of objects into meaningful components a crucial stage in robotic grasping. In this paper, we present a system designed to detect the graspable parts of objects for a specific task. Recognizing that everyday household items are typically grasped at certain sections for carrying, we created a database of these objects and their corresponding graspable parts. Building on the success of the Dynamic Graph CNN (DGCNN) network in segmenting object components, we enhanced this network to detect the graspable areas of objects. The enhanced network was trained on the compiled database, and the visual results, along with the obtained Intersection over Union (IoU) metrics, demonstrate its success in detecting graspable regions. It achieved a grand mean IoU (gmIoU) of 92.57% across all classes, outperforming established networks such as PointNet++ in part segmentation for this dataset. Furthermore, statistical analysis using analysis of variance (ANOVA) and T-test validates the superiority of our method.
Keywords: Robotic Grasp, Grasp Area, Point Cloud, Part Segmentation, Dynamic Graph CNN
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Type of Study: Closed - 2024 Special Issue on Applications of Deep Learning in Electrical and Electronic Engineerin | Subject: Machine Learning
Received: 2024/09/28 | Revised: 2025/01/03 | Accepted: 2024/12/22
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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.