| Summary: | 碩士 === 國立臺灣科技大學 === 材料科學與工程系 === 99 === Automatic visual inspection of defects plays an important role in technology industry. Currently, the light emitting doide (LED) has advantages of a long life, energy saving, and durability. Generally, its lens inspection is carried out by human eyes. Visual fatigue easily causes misjudgement; thus greatly reducing accuracy. This paper aims to apply image processing and classifiers to inspect lens defects of the high-power LED. Lens defects include scratch, particle, deformation and bubble. First, we use external ring light and a charge coupled device (CCD) to capture image. In image processing, the median filter is uesd to reduce impulse noise, and then image subtraction gives the defect region, and the hole filling in morphological operation obtains the complete shape of the defect. We choose roundness, thickness, shading as defect features. Seventy defect samples are collected, and the classifiers of back-propagation neural network and fuzzy neural network are used. The experiment result shows that with twenty training samples and fifty testing samples, the two classifiers can have classification rates of 100%. With some artificially generated feature values in testing samples, the experiment shows that the fuzzy neural network is more robust than the back-propagation neural network and has higher classification rate. Thus, the automatic defect inspection system works well for the high-power LED.
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