Novel Framework Based on HOSVD for Ski Goggles Defect Detection and Classification
| Autor: | Jing-Wein Wang, Chou-Chen Wang, Tu N. Nguyen, Ngoc Tuyen Le |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
Parallel projection Machine vision 02 engineering and technology Biochemistry Article Field (computer science) Analytical Chemistry law.invention Skiing law 0202 electrical engineering electronic engineering information engineering Humans Computer vision Electrical and Electronic Engineering Instrumentation business.industry parallel projection in opposite directions 020208 electrical & electronic engineering String (computer science) Atomic and Molecular Physics and Optics ski goggles lens Lens (optics) Visual inspection automatic optical inspection HOSVD adaptive energy analysis 020201 artificial intelligence & image processing Artificial intelligence Eye Protective Devices business Algorithms |
| Zdroj: | Sensors Volume 19 Issue 24 Sensors (Basel, Switzerland) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s19245538 |
| Popis: | No matter your experience level or budget, there is a great ski goggle waiting to be found.Goggles are an essential part of skiing or snowboarding gear to protect your eyes from harsh environmental elements and injury. In the ski goggles manufacturing industry, defects, especially on the lens surface, are unavoidable. However, defect detection and classification by visual inspection in the manufacturing process is very difficult. To overcome this problem, a novel framework based on machine vision is presented, named as the ski goggles lens defect detection, with five high-resolution cameras and custom-made lighting field to achieve a high-quality ski goggles lens image. Next, the defects on the lens of ski goggles are detected by using parallel projection in opposite directions based on adaptive energy analysis. Before being put into the classification system, the defect images are enhanced by an adaptive method based on the high-order singular value decomposition (HOSVD). Finally, dust and five types of defect images are classified into six types, i.e., dust, spotlight (type 1, type 2, type 3), string, and watermark, by using the developed classification algorithm. The defect detection and classification results of the ski goggles lens are compared to the standard quality of the manufacturer. Experiments using 120 ski goggles lens samples collected from the largest manufacturer in Taiwan are conducted to validate the performance of the proposed framework. The accurate defect detection rate is 100% and the classification accuracy rate is 99.3%, while the total running time is short. The results demonstrate that the proposed method is sound and useful for ski goggles lens inspection in industries. |
| Databáze: | OpenAIRE |
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