| dc.description.abstract | Automated optical inspection (AOI) is widely used in various industries, such as printed circuit board and flat panel display. In a traditional AOI system, digital image processing is applied to design an algorithm for the inspection. However, because of the limitation of AOI, manual visual inspection is still irreplaceable in certain cases, e.g. in glass manufacturing. Pollutions like dust particles and batting could reduce the performance of AOI system for glass. To overcome that, this study introduced an artificial intelligent technique into the AOI system for glass inspection. The inspection targets included two types of glass defects, namely scratch and chip. The goal was to find such defects on the glass specimens with dimensions of 85 mm × 53 mm × 2 mm.
In this study, the image acquisition system was optimized to capture the features of defects with the most suitable light source. For a better image quality, a jig and a black cloth were used to reduce the noise on the image. Such an image acquisition system could provide a high-quality image with defects, so that the image processing algorithm could analyze the information of defects on the specimens and detect defects.
In the image processing algorithm, 5 YOLOv4 variants were considered for the inspection application, namely YOLOv4, YOLOv4-tiny, YOLOv4-CSP, YOLOv4-P5, and YOLOv4-P6. The custom datasets were built to train these models. In order to have an objective annotation, it followed a procedure to label the custom datasets. After that, 5-fold cross validation was applied to compare each model’s performance. According to the validation results, YOLOv4 was selected for the application in this study as it had the highest accuracy and the ability of maintaining a great accuracy when the input image was resized to only 672 × 672 pixels. Then, two approaches were used to optimize the YOLOv4 model, namely fine-tuning and anchor box optimization. The latter method successfully improved the accuracy of YOLOv4. The most accurate model was the YOLOv4 optimized by anchor box and trained with the input size of 960 × 960 pixels. It had a precision of 98.4%, a recall of 91.7%, and mAP of 94.52% in the test dataset. Moreover, it could effectively exclude non-defect objects. With these results, the developed inspection system could detect defects as small as a scratch of 0.05-mm width and a chip of 0.1 mm. Furthermore, it could also detect an unclear scratch with only three-gray-level difference. Finally, the originally selected YOLOv4 model and its modification by anchor box optimization were applied to inspect two independent specimens of full-size images. The inspection results showed that both models successfully found defects that were not inspected by manual visual inspection. In comparison of the two models, the YOLOv4 model optimized by anchor box was more effective to detect defect and exclude non-defect objects, while the originally selected model of YOLOv4 was more efficient to inspect an entire specimen with a shorter time. | en_US |