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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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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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Detector of the Human Head in Image
Svoboda, Jakub ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
Detection of human head is an important part of person detection and identification algorithms. This thesis is focused on the detection of human head with methods based on neural networks. The majority the of conventional detectors can identify objects within a limited range of positions, whereas models based on neural networks offer a more robust approach. In this thesis we trained the current state-of-the-art models and compared their accuracy and speed. The most accurate model proved to be RetinaNet which has reached 85.15% AP. This detector can be used to improve current available algorithms for person detection, identification and tracking.
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Weapon Detection in an Image
Debnár, Pavol ; Drahanský, Martin (referee) ; Dvořák, Michal (advisor)
This thesis is focused on the topic of firearms detection in images. In the theoretic section, the explanation of the term firearm is covered, along with the definition of the most prevalent firearm categories. Then the concept of image noise and the ways it can hinder image detection is covered, along with ways of reducing it. Next, algorithms of image detection are introduced - first those which operate on the basis of neural nets - such as Convolutional Neural Nets and Single Shot Multibox Detection. The next section discusses classic algorithms of object detection such as HOG+SVM and SURF. After that, information on the used libraries and software is provided. The experimental part covers the designed algorithm and database. For detection, the HOG+SVM, SURF and SSD algorithms were used. All the algorithms are tested on the database and, if possible, on video. A final evaluation is provided, along with possible future development options.
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Protection of sensitive data contained in images
Mezina, Anzhelika ; Rajnoha, Martin (referee) ; Burget, Radim (advisor)
Tato bakalářská práce je zaměřena na využití hlubokého učení v bezpečnostním problému úniku citlivých informací ve formě obrazových dat. Pokusem o vyřešení tohoto problému bylo použití Single Shot Multibox Detectoru (SSD) a plně propojené sítě, poslední je mnohem rychlejší než jiné metody a může být použitá v praxi, kde je potřeba velmi rychlé analýzy příchozí a odchozí informace, například analýzy provozu sítě. V první části práce jsou popsané metody, které mohou být použité pro detekci klíčových slov. Druhá část obsahuje popis experimentu a dosažených výsledků pro dva modely neuronových sítí: Single Shot Multibox Detector a plně propojené sítě. Druhý model dosahuje uspokojivých vlastností jak z pohledu času zpracování tak i přesnosti a lze jej použít v praxi.
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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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Weapon Detection in an Image
Debnár, Pavol ; Drahanský, Martin (referee) ; Dvořák, Michal (advisor)
This thesis is focused on the topic of firearms detection in images. In the theoretic section, the explanation of the term firearm is covered, along with the definition of the most prevalent firearm categories. Then the concept of image noise and the ways it can hinder image detection is covered, along with ways of reducing it. Next, algorithms of image detection are introduced - first those which operate on the basis of neural nets - such as Convolutional Neural Nets and Single Shot Multibox Detection. The next section discusses classic algorithms of object detection such as HOG+SVM and SURF. After that, information on the used libraries and software is provided. The experimental part covers the designed algorithm and database. For detection, the HOG+SVM, SURF and SSD algorithms were used. All the algorithms are tested on the database and, if possible, on video. A final evaluation is provided, along with possible future development options.
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|
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Detector of the Human Head in Image
Svoboda, Jakub ; Orság, Filip (referee) ; Goldmann, Tomáš (advisor)
Detection of human head is an important part of person detection and identification algorithms. This thesis is focused on the detection of human head with methods based on neural networks. The majority the of conventional detectors can identify objects within a limited range of positions, whereas models based on neural networks offer a more robust approach. In this thesis we trained the current state-of-the-art models and compared their accuracy and speed. The most accurate model proved to be RetinaNet which has reached 85.15% AP. This detector can be used to improve current available algorithms for person detection, identification and tracking.
|
|
|
Protection of sensitive data contained in images
Mezina, Anzhelika ; Rajnoha, Martin (referee) ; Burget, Radim (advisor)
Tato bakalářská práce je zaměřena na využití hlubokého učení v bezpečnostním problému úniku citlivých informací ve formě obrazových dat. Pokusem o vyřešení tohoto problému bylo použití Single Shot Multibox Detectoru (SSD) a plně propojené sítě, poslední je mnohem rychlejší než jiné metody a může být použitá v praxi, kde je potřeba velmi rychlé analýzy příchozí a odchozí informace, například analýzy provozu sítě. V první části práce jsou popsané metody, které mohou být použité pro detekci klíčových slov. Druhá část obsahuje popis experimentu a dosažených výsledků pro dva modely neuronových sítí: Single Shot Multibox Detector a plně propojené sítě. Druhý model dosahuje uspokojivých vlastností jak z pohledu času zpracování tak i přesnosti a lze jej použít v praxi.
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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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