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Container Damage Detection and Classification Using Container Images

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2020

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Ieee

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Bilgisayar Mühendisliği Bölümü
Founded in 1992, our department has been dedicated to expanding and sharing knowledge, producing a line of highly skilled engineers, and inspiring innovation Department of Computer Engineering was founded in 1992 together with the Izmir Institute of Technology and started to admit students for the Master of Science Program. In 1999, the Department moved to the new campus in Gülbahçe-Urla and the undergraduate program commenced in the same year. Computer Engineering Doctorate Program started in 2014. Currently, the number of students admitted to the undergraduate program is 80. The Department of Computer Engineering offers a wide range of selective courses in its curriculum which enables the students to specialize in different areas of computer science and engineering. Our mission is to create a learning environment where academic research activities and projects are carried out in collaboration with the industry. In this atmosphere we aim to train researchers and engineers who are competent in the discipline, have proficiency in problem solving as well as good communication and organizational skills, committed to life-long learning and ethical values and sensitive to social issues.

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Abstract

In the logistics sector, digital transformation is of great importance in terms of competition. In the present case, container warehouse entry / exit operations are carried out manually by the logistics personnel including container damage detection. During container warehouse entry / exit process, the process of detecting damaged containers is carried out by the personnel and several minutes are required to upload to the IT system. The aim of our work is to automate the detection of damaged containers. This way, the mistakes made by the personnel will be eliminated and the process will be accelerated. In this work, we propose to use a convolutional neural network (CNN) that takes the container images and classify them as damaged or undamaged. We modeled the problem as a binary classification and employed different CNN models. The result we obtained shows that there is no single best method for the classification. It is shown how the dataset was created and how the parameters used in the layered structures affect the models employed in this study.

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container, image based classification, machine learning, deep learning, convolutional neural networks

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28th Signal Processing and Communications Applications Conference (SIU) -- OCT 05-07, 2020 -- ELECTR NETWORK

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