TY - GEN
T1 - An automatic system for defect detection in plastic crates for glass bottles.
AU - Juarez, Matthews
AU - Cruz, Anderson De La
AU - Vinces, Leonardo
AU - Vargas, Dante
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Automated system
KW - Image processing
KW - Inspection
KW - OpenCV
KW - Python
KW - Raspberry pi
KW - TensorFlow
UR - https://www.scopus.com/pages/publications/85179546457
U2 - 10.1109/CONIITI61170.2023.10324142
DO - 10.1109/CONIITI61170.2023.10324142
M3 - Contribución a la conferencia
AN - SCOPUS:85179546457
T3 - 2023 9th International Conference on Innovation and Trends in Engineering, CONIITI 2023 - Proceedings
BT - 2023 9th International Conference on Innovation and Trends in Engineering, CONIITI 2023 - Proceedings
A2 - Triana, Jenny Paola Hernandez
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Innovation and Trends in Engineering, CONIITI 2023
Y2 - 4 October 2023 through 6 October 2023
ER -