Integration of eye-tracking and object detection in a deep learning system for quality inspection analysis
- Title
- Integration of eye-tracking and object detection in a deep learning system for quality inspection analysis
- Author
- 서경민
- Keywords
- quality inspection; eye-tracking; object detection; deep learning; system integration
- Issue Date
- 2024-05-06
- Publisher
- OXFORD UNIV PRESS
- Citation
- JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING, v. 11, no 3, page. 158-173
- Abstract
- During quality inspection in manufacturing, the gaze of a worker provides pivotal information for identifying surface defects of a product. However, it is challenging to digitize the gaze information of workers in a dynamic environment where the positions and postures of the products and workers are not fixed. A robust, deep learning-based system, ISGOD (Integrated System with w orker’s Gaze and Object Detection), is proposed, which analyzes data to determine which part of the object is observed by integrating object detection and eye-tracking information in dynamic environments. The ISGOD employs a six-dimensional pose estimation algorithm for object detection, considering the location, orientation, and rotation of the object. Eye-tracking data were obtained fr om Tobii Glasses, which enable real-time video transmission and eye-movement tracking. A latency reduction method is proposed to ov ercome the time delays between object detection and eye-tracking information. Three evaluation indices, namely, gaze score, accuracy score, and concentration index are suggested for comprehensive analysis. Two experiments were conducted: a robustness test to confirm the suitability for real-time object detection and eye-tracking, and a trend test to analyze the difference in gaze movement between experts and novices. In the future, the proposed method and system can transfer the expertise of experts to enhance defect detection efficiency significantly.
- URI
- https://academic.oup.com/jcde/article/11/3/158/7665756https://repository.hanyang.ac.kr/handle/20.500.11754/190445
- ISSN
- 2288-5048
- DOI
- 10.1093/jcde/qwae042
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > INDUSTRIAL AND MANAGEMENT ENGINEERING(산업경영공학과) > Articles
- Files in This Item:
- 2024.5_.서경민_Integration of eye-tracking and object detection in a deep learning system for quality inspection analysis.pdfDownload
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