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 - 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.Conference Object Citation - WoS: 4Citation - Scopus: 13Classification and Tracking of Traffic Scene Objects With Hybrid Camera Systems(Institute of Electrical and Electronics Engineers Inc., 2018) Barış, İpek; Baştanlar, YalınIn a hybrid camera system combining an omnidirectional and a Pan-Tilt-Zoom (PTZ) camera, the omnidirectional camera provides 360 degree horizontal field-of-view, whereas the PTZ camera provides high resolution at a certain direction. This results in a wide field-of-view and high resolution camera system. In this paper, we exploit this hybrid system for real-time object classification and tracking for traffic scenes. The omnidirectional camera detects the moving objects and performs an initial classification using shape-based features. Concurrently, the PTZ camera classifies the objects using high resolution frames and Histogram of Oriented Gradients (HOG) features. PTZ camera also performs high-resolution tracking for the objects classified as the target class by the omnidirectional camera. The object types we worked on are pedestrian, motorcycle, car and van. Extensive experiments were conducted to compare the classification accuracy of the hybrid system with single camera alternatives.Conference Object Citation - Scopus: 5Detection and Classification of Vehicles From Omnidirectional Videos Using Temporal Average of Silhouettes(INSTICC, 2015) Karaimer, Hakkı Can; Baştanlar, YalınThis paper describes an approach to detect and classify vehicles in omnidirectional videos. The proposed classification method is based on the shape (silhouette) of the detected moving object obtained by background subtraction. Different from other shape based classification techniques, we exploit the information available in multiple frames of the video. The silhouettes extracted from a sequence of frames are combined to create an 'average' silhouette. This approach eliminates most of the wrong decisions which are caused by a poorly extracted silhouette from a single video frame. The vehicle types that we worked on are motorcycle, car (sedan) and van (minibus). The features extracted from the silhouettes are convexity, elongation, rectangularity, and Hu moments. The decision boundaries in the feature space are determined using a training set, whereas the performance of the proposed classification is measured with a test set. To ensure randomization, the procedure is repeated with the whole dataset split differently into training and testing samples. The results indicate that the proposed method of using average silhouettes performs better than using the silhouettes in a single frame.Conference Object Citation - WoS: 21Citation - Scopus: 33A Direct Approach for Human Detection With Catadioptric Omnidirectional Cameras(Institute of Electrical and Electronics Engineers Inc., 2014) Çınaroğlu, İbrahim; Baştanlar, YalınThis paper presents an omnidirectional vision based solution to detect human beings. We first go through the conventional sliding window approaches for human detection. Then, we describe how the feature extraction step of the conventional approaches should be modified for a theoretically correct and effective use in omnidirectional cameras. In this way we perform human detection directly on the omnidirectional images without converting them to panoramic or perspective image. Our experiments, both with synthetic and real images show that the proposed approach produces successful results. © 2014 IEEE.