Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Citation - Scopus: 1Pedestrian Equipment Anomaly Detection With Computer Vision in Warehouses(Avestia Publishing, 2024) Elçi,T.; Ünlü,M.Z.; Kantar,D.; Türker,A.Y.; Güney,H.; Ustaoğlu,A.The rapid growth of the logistics sector in recent years caused the expansion of warehouse areas and the increase in the number of equipment used. With the increase in these activities, the possibility of work accidents in warehouses also increases. In defiance of this situation, it has been determined that a real-time prediction system of pedestrian and equipment interaction is needed to ensure in-warehouse reliability. This system should address the urgent need to reduce the risk of work accidents and focus on the overall goal of reducing the possibility of work accidents in warehouse environments. To overcome this challenge, we propose a comprehensive Warehouse Anomaly Detection and Control System consisting of object detection, object tracking, action detection, and alarm classification components which will play an important role in increasing work safety in warehouse environments. YOLOv7 (You Only Look Once version 7) is a deep learning model that detects objects quickly and accurately in a single network pass. The deep learning-based Deep SORT algorithm used for object tracking provides a dynamic understanding of the warehouse environment by continuously storing these identified problems in real-time. The action detection part of this system is designed to identify and analyze actions and movements, recognizing anomalies and potential risks. In this part, the speed of pedestrians and equipment are detected utilization of 3D bounding boxes of objects and perspective transformation. The possible accident risks are measured using the intersection percentage of these areas, the magnitude of speed, the direction of the motion vector of pedestrian and equipment, and the distances between objects. Alert levels can be considered as encounter, near-miss, and emergency. Using this system in warehouses will reduce the risk of possible work accidents that may even result in death. © 2024, Avestia Publishing. All rights reserved.Conference Object Citation - WoS: 2Citation - Scopus: 3Impact of Variations in Synthetic Training Data on Fingerprint Classification(IEEE, 2019) İrtem, Pelin; İrtem, Emre; Erdoğmuş, NesliCreating and labeling data can be extremely time consuming and labor intensive. For this reason, lack of sufficiently large datasets for training deep structures is often noted as a major obstacle and instead, synthetic data generation is proposed. With their high acquisition and labeling complexity, this also applies to fingerprints. In the literature, a number of synthetic fingerprint generation systems have been proposed, but mostly for algorithm evaluation purposes. In this paper, we aim to analyze the use of synthetic fingerprint data with different levels of degradation for training deep neural networks. Fingerprint classification problem is selected as a case-study and the experiments are conducted on a public domain database, NIST SD4. A positive correlation between the synthetic data variation and the classification rate is observed while achieving state-of-the-art results.Conference Object Citation - Scopus: 2Label-Free Detection of Rare Cancer Cells Using Deep Learning and Magnetic Levitation Principle(SPIE, 2021) Delikoyun, Kerem; Demir, Ali Aslan; Tekin, Hüseyin CumhurMagnetic levitation is an effective tool for separating target cells within a heterogeneous solution by utilizing density differences among cell lines. However, magnetic levitation cannot be used to identify target cells which have similar density profile as the other cells in the solution. Therefore, accuracy of cell identification can dramatically reduce. In this study, we introduce, for the first time, the use of deep learning-based object detection approach for label-free identification of rare cancer cells within levitated cells. As a result, our novel and hybrid detection strategy could be used to identify circulating tumor cells for diagnosis and prognosis of cancer. © 2021 SPIE.