AI Machine Vision Automated Defect Detection System

Meina Qu

doi:10.5281/zenodo.13763253

Authors

Meina Qu City University of Seattle

DOI:

https://doi.org/10.5281/zenodo.13763253

ARK:

https://n2t.net/ark:/40704/JCTAM.v1n4a01

Disciplines:

Computer Science

Subjects:

Deep Learning

References:

28

Keywords:

Smart Manufacturing, Robotic Arm Grasping, Defect Detection, 2D/3D Vision Fusion, Deep Learning

Abstract

With the rapid development of smart manufacturing technologies, automated production has become an important trend in the transformation of the industrial chain. Among various automation applications, robotic arm grasping and visual inspection systems are the most widely used. This paper focuses on unstructured stacking scenarios and workpiece defect detection, and designs two deep learning-based vision systems. In terms of theoretical research, the study focuses on the fundamental knowledge and technical methods related to robotic arm grasping in unstructured environments and workpiece defect detection. To address the issue of grasping randomly stacked objects, a 2D/3D vision-based robotic arm grasping solution is proposed. This solution employs an eye-in-hand configuration, where RGB and depth images are captured by a stereo camera, and a depth feature extraction branch is added to the Mask R-CNN network to improve the accuracy of object detection and segmentation in complex scenes. For object localization, the segmented results are mapped to a 3D point cloud through RGB-D data registration, and the RANSAC and PCA algorithms are used to extract the target plane and bounding box, thereby obtaining the 6D pose information of the target. Combined with the hand-eye calibration results, the robotic arm can accurately grasp the target. Additionally, taking an automotive one-way clutch as an example, an automated defect detection system based on deep learning is designed. Using an industrial camera to capture images, the system utilizes a semantic segmentation network and a defect classification network to detect the number of teeth, copper sleeve, semicircular piece, and chamfer of the one-way clutch, thereby achieving automatic recognition of part defects. This paper integrates 2D image and 3D point cloud information, combined with deep learning methods, to explore robotic arm grasping and workpiece detection, providing new ideas and solutions for the development of smart manufacturing.

Author Biography

Meina Qu, City University of Seattle

City University of Seattle, USA.

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