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We introduce a real-time machine vision system we developed with the aim of detecting defects in functional textile fabrics with good precision at relatively fast detection speeds to assist in textile industry quality control. The system consists of image acquisition hardware and image processing software. The software we developed uses data preprocessing techniques to break down raw images to smaller suitable sizes. Filtering is employed to denoise and enhance some features. To generalize and multiply the data to create robustness, we use data augmentation, which is followed by labeling where the defects in the images are labeled and tagged. Lastly, we utilize YOLOv4 for localization where the system is trained with weights of a pretrained model. Our software is deployed with the hardware that we designed to implement the detection system. The designed system shows strong performance in defect detection with precision of [Formula: see text], and recall and [Formula: see text] scores of [Formula: see text] and [Formula: see text], respectively. The detection speed is relatively fast at [Formula: see text] fps with a prediction speed of [Formula: see text] ms. Our system can automatically locate functional textile fabric defects with high confidence in real time.

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Four solvent-non-solvent pairs (ethyl-acetate-cyclohexane, dichloromethane-cyclohexane, acetone-cyclohexane and dichloromethane-n-hexane) with different solubility parameter differences were chosen to prepare ethylcellulose microcapsules containing theophylline by using non-solvent-addition phase separation method. The results showed that the surface morphology and release behaviour of microcapsules were greatly affected by different solvent-non-solvent pairs. The surface of the microcapsules prepared from the system of high solubility parameter difference was more smooth than those from the systems of low solubility parameter difference. The release rate of the drug from microcapsules decreased with increasing solubility parameter difference of the preparative system. The determination of the wall thickness and porosity of the microcapsules could reasonably explain the release characteristics. The porosity of the microcapsules decreased with the increase of solubility parameter difference of the preparative system, but the wall thickness of the microcapsules showed a corresponding increase. The release of the drug from various ethylcellulose microcapsules fitted first-order kinetics with biphasic release profiles.