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Depth-Based Determination of a 3D Hand Position
Ondris, Ladislav ; Tinka, Jan (referee) ; Drahanský, Martin (advisor)
Cílem této práce je určení kostry ruky z hloubkového obrazu a jeho následné využití k rozpoznání statického gesta. Na vstupu je hloubkový obrázek, ve kterém je nejprve detekována ruka pomocí neuronové sítě Tiny YOLOv3. Následně je obrázek zbaven pozadí a z takto předzpracovaného obrázku je určena kostra ruky v podobě 21 klíčových bodů neuronovou sítí JGR-P2O. K rozpoznání gesta z klíčových bodů ruky byla navržena technika, která porovná kostru na vstupu s uživatelem definovanými gesty. Funkcionalita systému byla otestována na vytvořeném datasetu s více než čtyřmi tisíci obrázky.
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Detection of car approach speed using camera imge processing
Kovář, Jan ; Číka, Petr (referee) ; Šmirg, Ondřej (advisor)
The thesis deals with digital image processing, from the initial acquisition of digital picture frames, subsequent processing segmentation and algorithms to detect visual shapes on the scene. Image processing is a very broad topic, so here are analyzed for more understanding of the fundamental principles of perception and processing of video signals, image representation, his starting shooting through filters governing digital image processing methods to detect the objects in an image. It is also demonstrated by the size dependence of the object in the image on the distance from the camera, whereby we can determine the speed of approaching or moving away from the object. We will show you the specific determination of the distance we need to know the actual result size of the object. This is because the ratio between the size of the object depending on the distance is the same for each object. Finally, this work presents the resulting image frames for implementation using OpenCV library.
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Detection of Graffiti Tags in Image
Pavlica, Jan ; Hradiš, Michal (referee) ; Špaňhel, Jakub (advisor)
The thesis is focused on the possible utilization of current methods in the area of computer vision with the purpose of automatic detection of graffiti tags in the image. Graffiti tagsare the most common expression of graffiti, which serves as the author’s signature. In the thesis, state-of-the-art detection systems were tested; the most effective one is the Single Shot MultiBox Detector. The result has reached 75.7% AP.
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License Plate Recognition
Mrhač, Ondřej ; Sochor, Jakub (referee) ; Navrátil, Jan (advisor)
This thesis talks about problematics of licence plate detection, licence plate recognitionand my implementation for device i.MX 6 Series of NXP semiconductors s.r.o company. Model program for licence plate detection and recognition is written with help of OpenCV library and engine Tesseract and it’s successfully put into operation on this device. Afterwards program was measured his runtime on PC and i.MX6 Series device and those measurements were compared. At the end of this thesis were found the most demanding parts of the program. Future changes and improvements were proposed.
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Non-contact measurement of the dimensions of determination scales
Šemora, Petr ; Matoušek, Radomil (referee) ; Škrabánek, Pavel (advisor)
This thesis deals with non-contact measuring the dimensions of the sand lizard anal plate. First the thesis briefly summarizes the techniques used to measure object dimensions and the techniques used for image segmentation. Subsequently, the thesis provides a basic overview of neural networks and convolutional neural networks. The practical part describes a system for measuring the dimensions of the sand lizard anal plate. The proposed algorithms are implemented in a graphical user interface enabling automatic and manual measurements.
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Anti-Drone Perimeter Protection
Janík, Roman ; Dvořák, Michal (referee) ; Drahanský, Martin (advisor)
Developement of drone technology brings opportunities for many fields of human activity, but simultaneously brings security threats. A need to effectively face these threats arises. In this work is described the problematics and state-of-the-art methods for object detection in a video captured by moving camera. A system for detecting and locating a drone or a flock of drones has been proposed. Algorithm for detection is based on convolutional neural network, specifically on SSD algorithm. The convolutional neural network was trained on self-made dataset. The system was implemented using OpenCV library with possible algorithm acceleration on GPU using OpenCL. Created solution was tested on video.
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Assessment of Uncertainty of Neural Net Predictions in the Tasks of Classification, Detection and Segmentation
Vlasák, Jiří ; Kohút, Jan (referee) ; Herout, Adam (advisor)
This work focuses on comparing three widely used methods for improving uncertainty estimations: Deep Ensembles, Monte Carlo Dropout, and Temperature Scaling. These methods are applied to six computer vision models that are pretrained as well as trained from scratch. The models are then evaluated on computer vision datasets for classification, semantic segmentation, and object detection using a wide range of metrics. The models are also evaluated on distorted versions of these datasets to measure their performance on out-of-distribution data. These modified models achieve promising results. Ensembles outperform the other models by as high as 70 % in accuracy and 0.2 in IOU on the distorted MedSeg COVID-19 segmentation dataset while also outperforming the other models on the CIFAR-100 and FMNIST datasets.
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Autonomous vehicle for traffic situation model
Schneiderka, Dominik ; Boštík, Ondřej (referee) ; Janáková, Ilona (advisor)
This thesis describes development of autonomous car for Carrera 143 racing track. Main objective of a car is to stop when traffic light shows red, or when there is an obstacle infront of a car. This paper also describes electric schemes used to control the car and their placement on the car. Algorithms developed for image processing are developed for processing unit Raspberry Pi Zero and are written in C/C++ programming language. OpenCV library is used for image processing. All source codes were developed in Microsoft Visual Studio 2019.
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Pedestrians Detection in Traffic Environment by Machine Learning
Tilgner, Martin ; Klečka, Jan (referee) ; Horák, Karel (advisor)
Tato práce se zabývá detekcí chodců pomocí konvolučních neuronových sítí z pohledu autonomního vozidla. A to zejména jejich otestováním ve smyslu nalezení vhodné praxe tvorby datasetu pro machine learning modely. V práci bylo natrénováno celkem deset machine learning modelů meta architektur Faster R-CNN s ResNet 101 jako feature extraktorem a SSDLite s feature extraktorem MobileNet_v2. Tyto modely byly natrénovány na datasetech o různých velikostech. Nejlépší výsledky byly dosaženy na datasetu o velikosti 5000 snímků. Kromě těchto modelů byl vytvořen nový dataset zaměřující se na chodce v noci. Dále byla vytvořena knihovna Python funkcí pro práci s datasety a automatickou tvorbu datasetu.
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Visual detection of small objects using available tools in MATLAB
Sladký, Jiří ; Dobossy, Barnabás (referee) ; Appel, Martin (advisor)
This thesis investigates possibilities of small object detection in pictures using YOLO method, a deep learning algorithm available in MATLAB. In the thesis, a detector was designed and trained to detect cows from top-down view. A tool was created, that performs detection using the proposed model even on high resolution images and counts the present objects. A generator of synthetic images was programmed, which helped with training the model. Various experiments were performed that found the limits of YOLO and validated contribution of the proposed improvements.
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