Fakulta elektrotechniky a informatiky / Faculty of Electrical Engineering and Informatics
Stálý URI pro tuto komunituhttps://hdl.handle.net/10195/3847
Práce obhájené před rokem 2008 jsou uloženy pouze v kolekci Vysokoškolské kvalifikační práce
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Konferenční objektpeer-reviewedpostprint Omezený přístup Automated Dataset Enhancement Using GAN for Assessment of Degree of Degradation around Scribe(IEEE (Institute of Electrical and Electronics Engineers), 2023) Doležel, Petr; Pakosta, Marek; Rozsívalová, Veronika; Štursa, DominikCoil coating is a method of applying an organic coating material to a rolled metal strip substrate in a continuous automated process. It is used to provide a high quality, durable finish to a variety of surfaces. The degradation resistance of coil-coated materials is assessed according to European Standard EN 13523-8 by exposing a coil-coated test specimen to a salt fog at a defined temperature for a defined period of time. After this process, a sample is tested according to the International Organisation for Standardisation ISO 4628 standard to determine the degree of degradation. In this study, a GAN-based technique for automated training set enhancement is proposed to assess the degree of degradation around a scribe. The presented technique is capable of enhancing a manually generated dataset of images with synthetic samples to help refine the performance of the area degradation detector.Konferenční objektpeer-reviewedpostprint Omezený přístup Passage Detection of a Train via a Reference Point(Springer, 2023) Rejfek, Luboš; Pidanič, Jan; Štursa, Dominik; Nguyen, Tan N.; Tran, Phuong T.; Němec, Zdeněk; Zálabský, TomášA reference point detection system for position validation of a mobile object was developed for verification of experiments. The detection is based on a classic image processing algorithm and a processing algorithm using neural networks. Both approaches are compared. High-precision concept of the system is based on a camera sensor and automatic processing of video frames for position evalua-tion. The designed system was tested on a real application proving correct operation.Konferenční objektpeer-reviewedpostprint (accepted version) Otevřený přístup Grasping Point Detection Using Monocular Camera Image Processing and Knowledge of Center of Gravity(Springer Nature Switzerland AG, 2022) Štursa, Dominik; Doležel, Petr; Honc, DanielThe ability to grasp objects is one of the basic functions of modern industrial robots. In this article, the focus is placed on a system for processing the image provided by a robot visual perception system leading to the detection of objects grasping points. The proposed processing system is based on a multi-step method using convolutional neural networks (CNN). The first step is to use the first CNN to transform the input image into a schematic image with labeled objects centers of gravity, which then serves as a supporting input to the second CNN. In this second CNN, original input and supporting input images are used to obtain a schematic image containing the grasping points of the objects. This solution is further compared with a network providing grasping points directly from the input image. As a result, the proposed method provided a 0.7% improvement in the average intersection over union for all of the models.Konferenční objektpeer-reviewedpostprint (accepted version) Omezený přístup Classification of Polymers Based on the Degree of Their Transparency in SWIR Spectrum(Springer Nature Switzerland AG, 2022) Štursa, Dominik; Kopecký, Dušan; Roleček, Jiří; Doležel, Petr; Baruque Zanon, BrunoDetection, classification and sorting of polymeric particles is a common task required in recycling industry. In the proposed work, an innovative method for detection of polymeric particles and their classification is introduced. The method is based on evaluation of images of polymeric particles, obtained from short-wavelength infrared (SWIR) camera, by convolutional neural network (CNN). Compared to conventionally used spectroscopes or hyper-spectral imaging, this method utilizes single wavelength (1 050 nm) and a degree of polymer transparency serves as the main descriptor. Five different polymers (ABS, ABS-T, Nylon, PETG, PLA) in form of regular blocks (size 15 × 15 × 0.3 mm) were used in the experiment. In total 203 images (size 288 × 288 px) were prepared for CNN training and 67 for testing. Scalable ASP U-Net was tested in 6 combinations and their outputs were compared. According to used intersection over union metrics over all outputs, the topology with 64 filters and depth of 3 exhibited the best results.Konferenční objektpeer-reviewedpostprint (accepted version) Otevřený přístup Suitable ASP U-Net training algorithms for grasping point detection of nontrivial objects(IEEE (Institute of Electrical and Electronics Engineers), 2022) Doležel, Petr; Štursa, Dominik; Kopecký, DušanRobotic manipulation with nontrivial or irregular objects, which provide various types of grasping points, is of both academic and industrial interest. Recently, a powerful data-driven ASP U-Net deep neural network has been proposed to detect feasible grasping points of manipulated objects using RGB data. The ASP U-Net showed the ability to detect feasible grasping points with exceptional accuracy and more than acceptable inference times. So far, the network has been trained using an Adam optimizer only. However, in order to optimally utilize the potential of ASP U-Net, it was necessary to perform a systematic investigation of suitable training algorithms. Therefore, the aim of this contribution was to extend the impact of ASP U-Net by recommending suitable training algorithms and their parameters based on the result of training experiments.Konferenční objektpeer-reviewedpostprint (accepted version) Otevřený přístup Spectral Classification of Microplastics using Neural Networks: Pilot Feasibility Study(SciTePress - Science and Technology Publications, 2022) Doležel, Petr; Roleček, Jiří; Honc, Daniel; Štursa, Dominik; Baruque Zanon, BrunoMicroplastics, i.e. synthetic polymers that have particle size smaller than 5 mm, are emerging pollutants that are widespread in the environment. In order to monitor environmental pollution by microplastics, it is necessary to have available rapid screening techniques, which provide the accurate information about the quality (type of polymer) and quantity (amount). Spectroscopy is an indispensable method, if precise classification of individual polymers in microplastics is required. In order to contribute to the topic of autonomous spectra matching when using spectroscopy, we decided to demonstrate the quality and efficiency of neural networks. We adopted three neural network architectures, and we tested them for application to spectra matching. In order to keep our study transparent, we use publicly available dataset of FTIR spectra. Furthermore, we performed a deep statistical analysis of all the architectures performance and efficiency to show the suitability of neural networks for spectra matching. The results presented at the end of this article indicated the overall suitability of the selected neural network architectures for spectra matching in microplastics classification.Konferenční objektpeer-reviewedpostprint (accepted version) Omezený přístup Guidance of Unmanned Surface Vehicle Fleet Using Genetic Algorithm-Based Approach(SPRINGER INTERNATIONAL PUBLISHING AG, 2021) Dvořák, Miroslav; Doležel, Petr; Štursa, Dominik; Chouai, MohamedIn this contribution, we provide an approach to guidance of a fleet of unmanned surface vehicles. While the vehicles are themselves completely autonomous in their operational locomotion and obstacle avoidance, the task assignment for each vehicle is provided by a global planning process, based on the genetic algorithm. Various possibilities to the genetic algorithm are proposed and tested using a fleet of six vehicles dealing with a complex task.Konferenční objektpeer-reviewedpostprint (accepted version) Omezený přístup Medical Catheters Grasping Point Detection with Quality Control(SPRINGER INTERNATIONAL PUBLISHING AG, 2021) Štursa, Dominik; Doležel, Petr; Zanon, Bruno BThe ability to grasp objects is one of the basic functions of modern industrial robots. The emphasis of this paper is placed on the visual perception system, and in particular, on the data processing method leading to grasp point detection. The solution involved the design of a perceptual system in which it was necessary to use a SWIR sensor that can see through plastic bags and thus provide sufficient image information for possible processing by a neural network. The grasping point detection was tested with three convolutional neural network architectures. The method was evaluated by a generalized intersection over union (gIoU). The superior architecture was Attention U-Net, where gIoU reached 0.8522 in the case of the best model.Konferenční objektpeer-reviewedpostprint (accepted version) Otevřený přístup Multiple Objects Localization Using Image Segmentation with U-Net(IEEE (Institute of Electrical and Electronics Engineers), 2021) Štursa, Dominik; Doležel, Petr; Honc, DanielPrecise object localization in an industrial environment is a significant task affecting follow-up processes for a pick and place application. One of the solutions to effectively ensure the success of this task is to use modern methods of machine vision. Machine vision is still a highly evolving topic, in which the use of approaches based on convolutional neural networks is rising. And so in this contribution, an innovative engineering approach based on convolutional neural networks is proposed for an object localization task. The approach is based on an atypical image segmentation, where the individual objects are represented by two colored gradient circles. These circles represent significant parts of the object like its center or ending. Each object type (class) is determined by a specific color. By use of a local maxima finder, all circles in an image are transformed to points. With knowledge of these points the coordinates and rotations are calculated. The proposed approach was tested on a legitimate localization problem with 100% precision, more than 99.52% recall on the positioning task and with an average of 6 minutes angle variance per object.Konferenční objektpeer-reviewedpostprint (accepted version) Omezený přístup Airspace Object Detection Above the Guarded Area Using Segmentation Neural Network(Springer Nature Switzerland AG, 2021) Štursa, Dominik; Doležel, Petr; Merta, JanWith the increasing number of drones and unmanned aerial vehicles (UAVs), more emphasis is placed on guarding of the airspace around private and also public buildings. In this contribution authors are introducing a complex multi-step approach for aerial objects detection. Introduced process is composed of a few consecutive steps, where objects are cropped from original input with use of cropping pattern provided by task of image segmentation. These objects are then classified and evaluated as a threat or not. However, the emphasis here is placed on the segmentation part only. Neural network topology, adopted from U-Net architecture, was proposed. Case study was made and discussed in an effort to cover a large number of possible states. The results of a proposed convolutional neural network architecture were compared with the U-Net architecture. Applying of the convolutional neural network to the task of airspace object detection lead to sufficiently precise results, thanks to which it is possible to assume the possibility of its use in the proposed multi-step detection system in further work.
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