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 (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.Konferenční objektpeer-reviewedpostprint Omezený přístup One Step Deep Learning Approach to Grasp Detection in Robotics(Springer Science and Business Media, 2021) Doležel, Petr; Štursa, Dominik; Honc, DanielGrasp point detection is a necessary ability to handle for industrial robots. In recent years, various deep learning-based techniques for robotic grasping have been introduced. To follow this trend, we introduce a convolutional neural network-based approach for model-free one step method for grasp point detection. This method provides all feasible grasp points suitable for parallel grippers, based on a single RGB image of the scene. A case study, which shows the outstanding accuracy of the presented approach as well as its acceptable response time, is presented at the end of this contribution.Konferenční objektpeer-reviewedpostprint Omezený přístup Rapid 2D Positioning of Multiple Complex Objects for Pick and Place Application Using Convolutional Neural Network(IEEE (Institute of Electrical and Electronics Engineers), 2020) Doležel, Petr; Štursa, Dominik; Honc, DanielRobot guidance in an industrial environment is an important task to be solved in modern production facilities. A pick and place task is definitely one of the most common robot guidance issues to solve. In the beginning of the pick and place task, we need to perform a precise positioning of the objects of interest. In this contribution, an innovative engineering approach to multiple object positioning is proposed. The approach consists of two consecutive steps. At first, the original scene with objects of interest is transformed using a neural network. The output of this transformation is a schematic image, which represents the positions of the objects with gradient circles of various colors. Then, the positions of the gradient circles are determined by finding local maxima in the transformed image. The proposed approach is tested on a legitimate positioning problem with more than 99.8 % accuracy.Konferenční objektpeer-reviewedpostprint Otevřený přístup Counting Livestock with Image Segmentation Neural Network(Springer Nature Switzerland AG, 2020) Doležel, Petr; Štursa, Dominik; Honc, Daniel; Merta, Jan; Rozsívalová, Veronika; Beran, Ladislav; Hora, IvoLivestock farming industries, as well as almost any industry, want more and more data about the operation of their business and activities in order to make the right decisions. However, especially when considering very large animal farms, the precise and up-to-date information about the position and numbers of the animals is rather difficult to obtain. In this contribution, a novel engineering approach to livestock positioning and counting, based on image processing, is proposed. The approach is composed of two parts. Namely, a fully convolutional neural network for input image transformation, and a locator for animal positioning. The transformation process is designed in order to transform the original RGB image into a gray-scale image, where animal positions are highlighted as gradient circles. The locator then detects the positions of the circles in order to provide the positions of animals. The presented approach provides a precision rate of 0.9842 and a recall rate of 0.9911 with the testing set, which is, in combination with a rather suitable computational complexity, a good premise for the future implementation under real conditions.