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- Title
Inspection System for Vehicle Headlight Defects Based on Convolutional Neural Network.
- Authors
Moon, Chang-Bae; Lee, Jong-Yeol; Kim, Dong-Seong; Kim, Byeong-Man; Capria, Amerigo
- Abstract
This paper proposes a method to detect the defects in the region of interest (ROI) based on a convolutional neural network (CNN) after alignment (position and rotation calibration) of a manufacturer's headlights to determine whether the vehicle headlights are defective. The results were compared with an existing method for distinguishing defects among the previously proposed methods. One hundred original headlight images were acquired for each of the two vehicle types for the purpose of this experiment, and 20,000 high quality images and 20,000 defective images were obtained by applying the position and rotation transformation to the original images. It was found that the method proposed in this paper demonstrated a performance improvement of more than 0.1569 (15.69% on average) as compared to the existing method.
- Subjects
CONVOLUTIONAL neural networks; AUTOMOBILE defects; AUTOMOBILE lighting
- Publication
Applied Sciences (2076-3417), 2021, Vol 11, Issue 10, p4402
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app11104402