1 option
Quality Detection Model for Automotive Dashboard Based on an Enhanced Visual Model Tongji University, School of Automotive Studies and Clean En
- Format:
- Book
- Conference/Event
- Author/Creator:
- Luo, Erpai, author.
- Conference Name:
- Automotive Technical Papers (2022-01-01 : Warrendale, Pennsylvania, United States)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2022
- Summary:
- For an enterprise, product quality is the foundation of its further development. Therefore, how to detect the quality of the products produced by the assembly line and accurately identify the problematic parts has become an increasingly concerned issue for enterprises. In this paper, we propose a novel quality detection model combining the latest YOLOv5 model and convolutional neural network, which can further improve the recognition precision and accuracy of YOLOv5 on the basis of its lightweight and high recognition efficiency. The proposed model can meet the needs of complex quality problems that are difficult to detect directly in assembly-line products. In the experiment, our model can detect the automotive dashboard and judge whether the cable buckle is connected in place. The accuracy of each buckle in the picture being correctly detected is more than 98%, the classification accuracy is also expected to reach 98%
- Notes:
- Vendor supplied data
- Publisher Number:
- 2022-01-5081
- Access Restriction:
- Restricted for use by site license
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.