Automatic detection of dispersed defects in resin eyeglass based on machine vision technology

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FEA and Machine Learning Techniques for Hidden Structure Analysis

The hidden structure in this study contains two objects: one is the root system, the other is the non-visible bubble defect. The plant root system absorbs water and necessary nutrients and also synthesizes organic matter, which is essential for plant growth and regeneration. The defects in plexiglass are a prominent issue in the manufacturing industry, leading to the recall of unqualified products. Therefore, the investigation of both hidden structures can provide a deep understanding and useful information in their sectors. Current approaches involve soil-coring and mini-rhizotrons, which could damage roots or be time-consuming. Human vision examination has been widely used to inspect defects, but the results depend on operators. Modern techniques such as ground-penetrating radar and nuclear magnetic resonance have been employed, but the accuracy of results is limited to resolution, and the equipment is upscale. In this study, the infrared imaging method provides a non-destructive method: (1) to reveal the shape and position of the small roots system, such as sugarbeet roots; (2) to detect the diameter and depth of the non-visible bubble in plexiglass boards. The finite element analysis (FEA) methodology was implemented to validate the feasibility of applying infrared imaging to investigate the 1-mm-diameter roots and bubbles. A line scan method was developed to detect root structure based on the temperature difference between regions with soil and roots buried underneath. Support vector machine (SVM) and artificial neural network (ANN) were employed to predict root depth, and both reached accuracies over eighty percent. Three ANN models were developed to predict bubble size and achieved over ninety percent accuracies. The outcomes of the line scan method were evaluated qualitatively; also, the SVM and ANN models' results were compared by statistical tests. Future directions include (1) apply and optimize IR imaging to investigate different root systems and bubble defects in situ; (2) develop novel efficient denoise methodologies and optimize the predictive models to produce more accurate outcomes of roots and bubbles