ArticleSensors (Basel, Switzerland)2022
Integration of Deep Learning Network and Robot Arm System for Rim Defect Inspection Application.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
7 citing papers in PubMed, 24 citations in OpenAlex.
- SCMEO-DETR provides a lightweight and high efficiency solution for PCB defect detection based on RT-DETR.Scientific reports · 2026Article
- Real-Time and Fully Automated Robotic Stacking System with Deep Learning-Based Visual Perception.Sensors (Basel, Switzerland) · 2025Article
- Development of a Software and Hardware Complex for Monitoring Processes in Production Systems.Sensors (Basel, Switzerland) · 2025Article
- Improving Industrial Quality Control: A Transfer Learning Approach to Surface Defect Detection.Sensors (Basel, Switzerland) · 2025Article
- Two-Stream Network One-Class Classification Model for Defect Inspections.Sensors (Basel, Switzerland) · 2023Article
- Signal Processing and Machine Learning for Smart Sensing Applications.Sensors (Basel, Switzerland) · 2023Article
- A Big Coal Block Alarm Detection Method for Scraper Conveyor Based on YOLO-BS.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Automated inspection has proven to be the most effective approach to maintaining quality in industrial-scale manufacturing. This study employed the eye-in-hand architecture in conjunction with deep learning and convolutional neural networks to automate the detection of defects in forged aluminum rims for electric vehicles. RobotStudio software was used to simulate the environment and path trajectory for a camera installed on an ABB robot arm to capture 3D images of the rims. Four types of surface defects were examined: (1) dirt spots, (2) paint stains, (3) scratches, and (4) dents. Generative adversarial network (GAN) and deep convolutional generative adversarial networks (DCGAN) were used to generate additional images to expand the depth of the training dataset. We also developed a graphical user interface and software system to mark patterns associated with defects in the images. The defect detection algorithm based on YOLO algorithms made it possible to obtain results more quickly and with higher mean average precision (mAP) than that of existing methods. Experiment results demonstrated the accuracy and efficiency of the proposed system. Our developed system has been shown to be a helpful rim defective detection system for industrial applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.