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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Článekpeer-reviewedpostprint Omezený přístup Genetic Algorithm-Based Task Assignment for Fleet of Unmanned Surface Vehicles in Dynamically Changing Environment(Taylor & Francis Inc, 2023) Dvořák, Miroslav; Doležel, Petr; Štursa, Dominik; Chouai, MohamedUnmanned vehicles are gaining the attention of professional operators and the general public. The implementation of unmanned vehicles is evident in, among other fields, emergency management, agriculture, traffic monitoring, post-disaster operations, and delivery of goods. Naturally, a group of unmanned vehicles can cooperatively complete operations more proficiently than a single vehicle. However, several issues must be resolved before a stable and reliable group of unmanned vehicles can be generally deployed to solve tasks in civil infrastructures and in industrial facilities. Here, a framework for the guidance of a fleet of unmanned surface vehicles is proposed. The framework utilizes several levels of control, namely Global Planning Level, Local Planning Level, and Low-Level Control. While the individual vehicles are completely autonomous in their operational locomotion and obstacle avoidance (low-level control and local planning), the task assignment for each vehicle (or group of them) is provided by a global planning process, based on the genetic algorithm. The framework provides a concept to solve complex tasks for the fleet of unmanned surface vehicles (USVs). This includes, but is not necessarily limited to, a dynamically changing environment, different types of USVs with special abilities, multiple types of areal restrictions and obstacles, different restrictions for individual USVs, cooperation of multiple USVs to solve their subtasks, energy consumption optimization, etc. The framework can be advantageously applied to tasks such as warehouse logistics, surface maintenance, area exploration, etc. At the end of the study, the application of the framework is presented using a simulated example of cooperative problem solving using six vehicles.Článekpeer-reviewedpublished Otevřený přístup Centroid based person detection using pixelwise prediction of the position(Elsevier Science BV, 2022) Doležel, Petr; Škrabánek, Pavel; Štursa, Dominik; Zanon, Bruno Baruque; Adrian, Hector Cogollos; Kryda, PavelImplementations of person detection in tracking and counting systems tend towards processing of orthogonally captured images on edge computing devices. The ellipse-like shape of heads in orthogonally captured images inspired us to predict head centroids to determine positions of persons in images. We predict the centroids using a fully convolutional network (FCN). We combine the FCN with simple image processing operations to ensure fast inference of the detector. We experiment with the size of the FCN output to further decrease the inference time. We compare the proposed centroid-based detector with bounding box-based detectors on head detection task in terms of the inference time and the detection performance. We propose a performance measure which allows quantitative comparison of the two detection approaches. For the training and evaluation of the detectors, we form original datasets of 8000 annotated images, which are characterized by high variability in terms of lighting conditions, background, image quality, and elevation profile of scenes. We propose an approach which allows simultaneous annotation of the images for both bounding box-based and centroid-based detection. The centroid-based detector shows the best detection performance while keeping edge computing standards.Článekpeer-reviewedpublished Otevřený přístup Sequence of U-Shaped Convolutional Networks for Assessment of Degree of Delamination Around Scribe(Atlantis Press, 2022) Rozsívalová, Veronika; Doležel, Petr; Štursa, Dominik; Rozsíval, PavelThe application of protective layers is the primary method of keeping metallic structures resistant to degradation. The measurement of the layer resistance to delamination is one of the important indicators of the protection quality. Therefore, ISO 4628 standard has been issued to handle and quantify the main coating defects. Here, an innovative assessment of degree of delamination around a scribe according to ISO 4628 standard has been practically realized. It utilizes an computer-driven deep learning-based method. The assessment method is composed of two shallow U-shaped convolutional networks in a row; the first for preliminary and the second for refined detection of delamination area around a scribe. The experiments performed on 586 samples showed that the proposed sequence of U-shaped convolutional networks meets the edge computing standards, provides good generalization capability, and provides precise delamination area detection for a large variability of surfaces.Článekpeer-reviewedpublished version Otevřený přístup Optimization of a Depiction Procedure for an Artificial Intelligence-Based Network Protection System Using a Genetic Algorithm(MDPI, 2021) Doležel, Petr; Holík, Filip; Merta, Jan; Štursa, DominikThe current demand for remote work, remote teaching and video conferencing has brought a surge not only in network traffic, but unfortunately, in the number of attacks as well. Having reliable, safe and secure functionality of various network services has never been more important. Another serious phenomenon that is apparent these days and that must not be discounted is the growing use of artificial intelligence techniques for carrying out network attacks. To combat these attacks, effective protection methods must also utilize artificial intelligence. Hence, we are introducing a specific neural network-based decision procedure that can be considered for application in any flow characteristic-based network-traffic-handling controller. This decision procedure is based on a convolutional neural network that processes the incoming flow characteristics and provides a decision; the procedure can be understood as a firewall rule. The main advantage of this decision procedure is its depiction process, which has the ability to transform the incoming flow characteristics into a graphical structure. Graphical structures are regarded as very efficient data structures for processing by convolutional neural networks. This article's main contribution consists of the development and improvement of the depiction process using a genetic algorithm. The results presented at the end of the article show that the decision procedure using an optimized depiction process brings significant improvements in comparison to previous experiments.Článekpeer-reviewedpublished version Otevřený přístup New End-to-End Strategy Based on DeepLabv3+Semantic Segmentation for Human Head Detection(MDPI, 2021) Chouai, Mohamed; Doležel, Petr; Štursa, Dominik; Němec, ZdeněkIn the field of computer vision, object detection consists of automatically finding objects in images by giving their positions. The most common fields of application are safety systems (pedestrian detection, identification of behavior) and control systems. Another important application is head/person detection, which is the primary material for road safety, rescue, surveillance, etc. In this study, we developed a new approach based on two parallel Deeplapv3+ to improve the performance of the person detection system. For the implementation of our semantic segmentation model, a working methodology with two types of ground truths extracted from the bounding boxes given by the original ground truths was established. The approach has been implemented in our two private datasets as well as in a public dataset. To show the performance of the proposed system, a comparative analysis was carried out on two deep learning semantic segmentation state-of-art models: SegNet and U-Net. By achieving 99.14% of global accuracy, the result demonstrated that the developed strategy could be an efficient way to build a deep neural network model for semantic segmentation. This strategy can be used, not only for the detection of the human head but also be applied in several semantic segmentation applications.Článekpeer-reviewedpublished version Otevřený přístup Memory Efficient Grasping Point Detection of Nontrivial Objects(IEEE (Institute of Electrical and Electronics Engineers), 2021) Doležel, Petr; Štursa, Dominik; Kopecký, Dušan; Jecha, JiříRobotic manipulation with a nontrivial object providing various types of grasping points is of an industrial interest. Here, an efficient method of simultaneous detection of the grasping points is proposed. Specifically, two different 3 degree-of-freedom end effectors are considered for simultaneous grasping. The method utilizes an RGB data-driven perception system based on a specifically designed fully convolutional neural network called attention squeeze parallel U-Net (ASP U-Net). ASP U-Net detects grasping points based on a single RGB image. This image is transformed into a schematic grayscale frame, where the positions and poses of the grasping points are coded into gradient geometric shapes. In order to approve the ASP U-Net architecture, its performance was compared with nine competitive architectures using metrics based on generalized intersection over union and mean absolute error. The results indicate its outstanding accuracy and response time. ASP U-Net is also computationally efficient enough. With a more than acceptable memory size (77 MB), the architecture can be implemented using custom single-board computers. Here, its capabilities were tested and evaluated on the NVIDIA Jetson NANO platform.Článekpeer-reviewedpublished version Otevřený přístup Person Detection for an Orthogonally Placed Monocular Camera(Hindawi limited, 2020) Škrabánek, Pavel; Doležel, Petr; Němec, Zdeněk; Štursa, DominikCounting of passengers entering and exiting means of transport is one of the basic functionalities of passenger flow monitoring systems. Exact numbers of passengers are important in areas such as public transport surveillance, passenger flow prediction, transport planning, and transport vehicle load monitoring. To allow mass utilization of passenger flow monitoring systems, their cost must be low. As the overall price is mainly given by prices of the used sensor and processing unit, we propose the utilization of a visible spectrum camera and data processing algorithms of low time complexity to ensure a low price of the final product. To guarantee the anonymity of passengers, we suggest orthogonal scanning of a scene. As the precision of the counting is relevantly influenced by the precision of passenger recognition, we focus on the development of an appropriate recognition method. We present two opposite approaches which can be used for the passenger recognition in means of transport with and without entrance steps, or with split level flooring. The first approach is the utilization of an appropriate convolutional neural network (ConvNet), which is currently the prevailing approach in computer vision. The second approach is the utilization of histograms of oriented gradients (HOG) features in combination with a support vector machine classifier. This approach is a representative of classical methods. We study both approaches in terms of practical applications, where real-time processing of data is one of the basic assumptions. Specifically, we examine classification performance and time complexity of the approaches for various topologies and settings, respectively. For this purpose, we form and make publicly available a large-scale, class-balanced dataset of labelled RGB images. We demonstrate that, compared to ConvNets, the HOG-based passenger recognition is more suitable for practical applications. For an appropriate setting, it defeats the ConvNets in terms of time complexity while keeping excellent classification performance. To allow verification of theoretical findings, we construct an engineering prototype of the system.Článekpeer-reviewedpublished version Otevřený přístup Development of image procesing system for person detection(MM spektrum, 2020) Štursa, Dominik; Honc, Daniel; Doležel, PetrWith the rise of modern technology in computer science and engineering, as well as with the growing population in big cities around the world, many new approaches for person detection have become a very interesting and demanding topic. Person detection is a necessary building block for people monitoring systems and, therefore, various detection methods must be inspected comprehensively in order to select the one with the most suitable performance and accuracy. In this paper, a set of different image processing techniques applied to images captured from a high angle were used for people detection. To be more specific, selected feature extraction techniques, like edge detectors, local binary patterns, pixel intensities or histograms of oriented gradients, were used in combination with several classification algorithms. The combinations of each feature extractor and its best classifier were selected for performance comparison. As a result of the comparison, the most suitable image processing method for person detection in high angle image is presented at the end of the paper.