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-reviewedpostprint Omezený přístup CSU-Net: Contour Semantic Segmentation Self-Enhancement for Human Head Detection(IEEE (Institute of Electrical and Electronics Engineers), 2023) Chouai, Mohamed; Doležel, PetrThe computer vision community has made tremendous progress in solving a variety of semantic image understanding tasks, such as classification and segmentation. With the advancement of imaging technology and hardware, image semantic segmentation, through the use of deep learning, is among the most common topics which have been worked on in the last decade. However, image semantic segmentation suffers from several drawbacks such as insufficient detection of object boundaries. In this study, we present a new convolutional neural network architecture called CSU-Net that aims to self-enhance the results of semantic segmentation. The proposed model consists of two strongly concatenated encoder-decoder blocks. With this design, we reduced requirements on computing power and memory size to decrease costs and increase the training/prediction speed. This study also demonstrates the advantage of the proposed system for small training data sets. The proposed approach has been implemented on our private dataset, as well as on a publicly available dataset. A comparative analysis was carried out with four popular segmentation models and three other recently introduced architectures to show the efficiency of the proposed system. CSU-Net outperformed the other competing neural networks that we considered for the comparative study. As an example, it succeeded in improving the traditional U-Net result by approximately 50% in mean Intersection over Union (mIoU) for both tested datasets. Based on our experience, the CSU-Net can improve results of semantic segmentation in many applications.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 Autoencoder and Modified YOLOv3 Based Firearms Object Detection in X-ray Baggage Images to Enhance Aviation Safety(SPRINGER INTERNATIONAL PUBLISHING AG, 2021) Chouai, Mohamed; Merah, Mostefa; Sancho-GOmez, Jose-Luis; Doležel, PetrAt airports and especially the baggage inspection task, the vital question that the human operator must answer is how to strike a balance between security screening, facilitation in a confined space, the good imypression of passengers through their passage, and speed of inspection. In order to help them reinvent their approach to control in such an environment, the help of automatic intelligent tools is necessary. This paper proposes firearms object detection based on modified YOLOv3 and autoencoder for security defense in dual X-ray images. The object detection is performed by a modified version of YOLOv3, to detect all the objects presented in the baggage. The object features are carried out by an autoencoder. The classification is performed by a Multi-Layer Perceptron (MLP) to classify a new object as a weapon or not. The proposed system has shown high efficiency in detecting firearms with a precision of 96.50%.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.