Abstract
To solve the problem of high-wage employment and unemployment that is constantly occurring in industrial sites, we designed a real-time anomaly detection system based on YOLOv4 to automate the detection of defective products at actual manufacturing sites. This contributes to reducing labor costs and increasing work efficiency in the field. It also contributes to manufacturing data collection and smart factory system construction by utilizing the established system.
| Original language | English |
|---|---|
| Pages (from-to) | 130-136 |
| Number of pages | 7 |
| Journal | WSEAS Transactions on Electronics |
| Volume | 13 |
| DOIs | |
| State | Published - 2022 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 8 Decent Work and Economic Growth -
SDG 9 Industry, Innovation, and Infrastructure
Keywords
- AI Deep Learning
- Anomaly Detection
- Edge Computing
- Manufacturing Data Platform
- Smart Factory
- Supervised Learning
- YOLOv4
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