Search Results (3)

Background: The incidence of chemical ocular trauma after accidentally instilling the “wrong” eyedrops is still frequent, but cases resulting from stamper ink refills are rare. Case Presentation: A 73-year-old man presented to our emergency department with a history of inadvertently instilling stamper ink refill into both eyes (BEs) instead of artificial tears. Immediate irrigation and evaluation were performed. The initial visual acuity (VA) was 0.4 in the right eye (RE) and 0.8 in the left eye (LE). Slit lamp examination showed edema palpebra with periocular blue staining in BEs, chemotic conjunctiva with a much darker color in the RE than the LE, and epithelial defects with a positive fluorescein test in BEs. A diagnosis of bilateral corneal abrasion and chemotic conjunctiva was established. Ten hours after the emergency visit, RE VA decreased to 0.2, and corneal edema was found during the follow-up examination. Medications including levofloxacin antibiotic, sodium hyaluronate, sodium chloride, combined polymyxin sulfate–neomycin sulfate and dexamethasone eyedrops, mefenamic acid, and ascorbic acid tablets were prescribed. The RE corneal edema still occurred, and the endothelial cell count was 1952 and 987 cells/mm² in the RE and LE at the one-week follow-up. After three weeks, corneal edema had fully resolved, and the VA was 0.4 and 0.8 in the RE and LE, respectively. Conclusions: This case report adds to the spectrum of the continuing problem of chemical ocular trauma after mistakenly instilling the eyedrops. Promoting and changing to different packages for non-ophthalmic products in plastic bottles mimicking eyedroppers is essential to minimize these injuries. Full article

Recycled glass has been the focus of attention owing to its role in reducing plastic waste and further increasing the demand for glass containers. Cosmetics glass bottles require strict quality inspections because of the frequent handling, safety concerns, and other factors. During manufacturing, glass bottles sometimes develop chips on the top surface, rim, or screw threads of the bottle mouth. Conventionally, these chips are visually inspected by inspectors; however, this process is time consuming and prone to inaccuracies. To address these issues, automatic inspection using image processing has been explored. Existing methods, such as dynamic luminance value correction and ring-shaped inspection gates, have limitations: the former relies on visible light, which is strongly affected by natural light, and the latter acquires images directly from above, resulting in low accuracy in detecting chips on the lower part of screw threads. To overcome these challenges, this study proposes a method that combines infrared backlighting and image processing to determine the range of screw threads and detect chips accurately. Experiments were conducted in an experimental environment replicating an actual factory production line. The results confirmed that the detection accuracy of chipping was 99.6% for both good and defective bottles. This approach reduces equipment complexity compared to conventional methods while maintaining high inspection accuracy, contributing to the productivity and quality control of glass bottle manufacturing. Full article

With the increasing utilization of plastic bottles in the fast-moving consumer goods industry, the efficiency and accuracy of the bottle defect inspection process becomes very important for quality assurance. Deep-learning-based inspection is accurate and robust, but at the same time data hogging and computationally expensive. Thus, it is not feasible for fast inspection. Therefore, this paper presents an efficient and fast machine-vision-based system for inspecting plastic bottle defects. The system is composed of a chamber which has a camera and illuminators to capture high-resolution images in controlled lighting conditions. Captured images are processed by using simple image processing techniques to identify multiple defects such as seated cap, dents on the body and label alignment on the plastic. The experimental results show that the proposed system is 95% accurate in determining a range of bottle defects. It is highly feasible for fast inspection and does not require high computation power and a large amount of training data. Full article