This study developed a defect-detecting system for automotive wheel nuts. We proposed an image processing method using OpenCV for efficient defect-detection of automotive wheel nuts. Image processing method focused on noise removal, ratio adjustment, binarization, polar coordinate system formation, and orthogonal coordinate system conversion. Through data collection, preprocessing, object detection model training, and testing, we established a system capable of accurately classifying defects and tracking their positions. There are four defect types. Types 1 and 2 defects are defects of products where the product is completely broken circumferentially. Types 3 and 4 defects are defects are small circumferential dents and scratches in the product. We utilized Faster R-CNN and YOLOv8 models to detect defect types. By employing effective preprocessing and post-processing steps, we enhanced the accuracy. In the case of Fast RCNN, AP values were 0.92, 0.93, 0.76, and 0.49 for types 1, 2, 3, and 4 defects, respectively. The mAP was 0.77. In the case of YOLOv8, AP values were 0.78, 0.96, 0.8, and 0.51 for types for types 1, 2, 3, and 4 defects, respectively. The mAP was 0.76. These results could contribute to defect detection and quality improvement in the automotive manufacturing sector.

Bell’s palsy is a disease that occurs primarily between ages of 15 and 60, especially in middle-aged individuals. Although this disease gradually recovers within weeks to months, recurrence and permanent sequelae are possible. Its causes are diverse and unclear. Appropriate treatment is unknown, threatening lives of patients with this condition. In this study, we measured the degree of facial paralysis in a model of Bell’s palsy patients using OpenCV and the H.B grade measurement method and classified measured values according to H.B grade classification. This enabled prediction of the type and risk of diseases that might occur depending on the degree of facial paralysis. Additionally, we utilized more coordinate data to confirm movement of facial muscles by region to address limitations of the Nottingham system measurement method. We graded the level of this movement to enable intuitive confirmation and confirmed differences between existing Nottingham system and the H.B grade. This simple system could determine the level of paralysis in patients with Bell’s palsy and their corresponding risk level for related diseases. It enables information on causative disease of patients with Bell’s palsy to be quickly obtained, enabling prompt treatment and support.