• KIPS Transactions on Software and Data Engineering
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• v.11 no.5
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• pp.211-220
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• 2022

Recently, research on smart factories triggered by the 4th industrial revolution is being actively conducted. Accordingly, the manufacturing industry is conducting various studies to improve productivity and quality based on deep learning technology with robust performance. This paper is a study on the method of detecting tire surface defects in the visual inspection stage of the tire manufacturing process, and introduces a tire surface defect detection method using a depth image acquired through a 3D camera. The tire surface depth image dealt with in this study has the problem of low contrast caused by the shallow depth of the tire surface and the difference in the reference depth value due to the data acquisition environment. And due to the nature of the manufacturing industry, algorithms with performance that can be processed in real time along with detection performance is required. Therefore, in this paper, we studied a method to normalize the depth image through relatively simple methods so that the tire surface defect detection algorithm does not consist of a complex algorithm pipeline. and conducted a comparative experiment between the general normalization method and the normalization method suggested in this paper using YOLO V3, which could satisfy both detection performance and speed. As a result of the experiment, it is confirmed that the normalization method proposed in this paper improved performance by about 7% based on mAP 0.5, and the method proposed in this paper is effective.

• The Journal of the Korea Contents Association
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• v.17 no.11
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• pp.303-311
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• 2017

Safety accidents in railway work continue to increase every year; Engineer's negligence, trackside worker's sensory deprivation and signalman's mistake are the main reasons of such incidents. We consider this problem by far the most urgent matter in railway work because of its steady increase and risk of taking a person's life. Based on that account, a new alarm system has developed, that is called Railway Access Alarm System, to allow railway workers to sense the access of trains with not only vision, but also hearing. The detector device of this system is installed on both sides of the track locating 1.5km from the workplace. When the train enters the place, the detector device can sense the entering, sending the detect sign of train to the alarm unit, then the alarm unit warns the workers by the LED lighting and sirens. This system has several advantages compared to previous systems. First, it recognizes the train at a long distance. Secondly, there is no need for wiring work since it is a wireless system. At last, the system works by rechargeable batteries and solar charger so that it is installed in the work places where there is no external power supply. Moreover, it is proven that the system is 100% reliable by the successful on-the spot inspection evaluating the capability.

• Journal of Food Hygiene and Safety
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• v.34 no.1
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• pp.1-12
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• 2019

The health benefits associated with consumption of fresh produce have been clearly demonstrated and encouraged by international nutrition and health authorities. However, since fresh produce is usually minimally processed, increased consumption of fresh fruits and vegetables has also led to a simultaneous escalation of foodborne illness cases. According to the report by the World Health Organization (WHO), 1 in 10 people suffer from foodborne diseases and 420,000 die every year globally. In comparison to other processed foods, fresh produce can be easily contaminated by various routes at different points in the supply chain from farm to fork. This review is focused on the identification and characterization of possible sources of foodborne illnesses from chemical, biological, and physical hazards and the applicable methodologies to detect potential contaminants. Agro-chemicals (pesticides, fungicides and herbicides), natural toxins (mycotoxins and plant toxins), and heavy metals (mercury and cadmium) are the main sources of chemical hazards, which can be detected by several methods including chromatography and nano-techniques based on nanostructured materials such as noble metal nanoparticles (NMPs), quantum dots (QDs) and magnetic nanoparticles or nanotube. However, the diversity of chemical structures complicates the establishment of one standard method to differentiate the variety of chemical compounds. In addition, fresh fruits and vegetables contain high nutrient contents and moisture, which promote the growth of unwanted microorganisms including bacterial pathogens (Salmonella, E. coli O157: H7, Shigella, Listeria monocytogenes, and Bacillus cereus) and non-bacterial pathogens (norovirus and parasites). In order to detect specific pathogens in fresh produce, methods based on molecular biology such as PCR and immunology are commonly used. Finally, physical hazards including contamination by glass, metal, and gravel in food can cause serious injuries to customers. In order to decrease physical hazards, vision systems such as X-ray inspection have been adopted to detect physical contaminants in food, while exceptional handling skills by food production employees are required to prevent additional contamination.