An automatic system for defect detection in plastic crates for glass bottles.
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Issue Date
2023-01-01
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2023 9th International Conference on Innovation and Trends in Engineering, CONIITI 2023 - ProceedingsDOI
10.1109/CONIITI61170.2023.10324142Abstract
The project describes the design and implementation of an automatic system for detecting defects in plastic crates for glass bottles. In all companies there is damage and defects in their cases, crates, or containers due to constant use, as they are reusable, and therefore this problem causes various economic losses and a decrease in production, especially in beverage companies. This system was designed to solve and prevent the crates from having defects in their base and containing waste inside, to obtain less product losses in the bottle packaging area. In this research, it is proposed to design the automatic system, which consists of training a convolutional neural network with a database of 136 photographs of waste and defects in the boxes that will be taken by the HQ Raspberry Camera; then programmed into the Raspberry the process of activating the engine so that the box is moved to the point where it will be detected by the photoelectric sensor and the inspection is performed; and finally it is classified indicating whether or not it is in optimal conditions. This is developed in Python using different libraries such as OpenCV, TensorFlow, Tkinter among others. Our results show that the classification and object detection accuracy reached 91.84% out of a bank of 264 tests performed.Type
info:eu-repo/semantics/articleRights
info:eu-repo/semantics/embargoedAccessLanguage
engae974a485f413a2113503eed53cd6c53
10.1109/CONIITI61170.2023.10324142
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