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.Article Citation - WoS: 4Citation - Scopus: 4Fluence (uv Dose) Distribution Assessment of Uv-C Light at 254 Nm on Food Surfaces Using Radiochromic Film Dosimetry Integrated With Image Processing and Convolutional Neural Network (cnn)(Elsevier Sci Ltd, 2023) Cankal, Yadigar Seyfi; Ünlütürk, Mehmet S.; Ünlütürk, SevcanUniform Fluence (UV Dose) distribution on food surfaces is essential for an effective UV process design. In this study, the use of radiochromic films (RCFs) with a computer vision system (CVS) integrating image processing and Convolutional Neural Network (CNN) is proposed as an alternative method to assess Fluence distribution of UV-C light at 254 nm on food surfaces. The color difference of RCFs exposed to different UV irradiance and exposure times was correlated with Fluence. The validity of the developed methodology was proved by applying it to the surface of apple fruits of different shapes and sizes. A linear relationship was found between the color difference of RCF and Fluence. The maximum Fluence to be determined using RCFs was similar to 60 mJ/cm(2). The color of the films after UV irradiation remained stable for up to 15 days in darkness when stored at room and refrigeration temperatures. The results showed that RCF can be used as an alternative UV dosimeter.Article Citation - WoS: 43Citation - Scopus: 47Semantic Segmentation of Outdoor Panoramic Images(Springer, 2021) Orhan, Semih; Baştanlar, YalınOmnidirectional cameras are capable of providing 360. field-of-view in a single shot. This comprehensive view makes them preferable for many computer vision applications. An omnidirectional view is generally represented as a panoramic image with equirectangular projection, which suffers from distortions. Thus, standard camera approaches should be mathematically modified to be used effectively with panoramic images. In this work, we built a semantic segmentation CNN model that handles distortions in panoramic images using equirectangular convolutions. The proposed model, we call it UNet-equiconv, outperforms an equivalent CNN model with standard convolutions. To the best of our knowledge, ours is the first work on the semantic segmentation of real outdoor panoramic images. Experiment results reveal that using a distortion-aware CNN with equirectangular convolution increases the semantic segmentation performance (4% increase in mIoU). We also released a pixel-level annotated outdoor panoramic image dataset which can be used for various computer vision applications such as autonomous driving and visual localization. Source code of the project and the dataset were made available at the project page (https://github.com/semihorhan/semseg-outdoor-pano). © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.