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1	Detekce a rozpoznání zbraně ve scéně / Detection and Recognition of Gun in a Scene Stuchlík, David January 2020 (has links) The aim of the diploma thesis is to design an algorithm for detection and recognition of the type of gun in the image. Firstly, the existing methods and techniques for detecting the various objects are briefly introduced in the text of the thesis however, the methods are primarily focused on guns. Next, the basics of neural networks are briefly outlined, followed by an overview of the most common detectors for deep neural networks. The second half of the thesis is devoted to the implementation of an application for generating images based on a 3D model of a gun, the creation of a data file and learning of a neural network. Finally, the results obtained, which clearly indicate that in order to cover a huge variation of real weapons, is necessary to generate a large amount of training data based on many different 3D models, are briefly summarized in the conclusion of the thesis.
2	A Client-Server Solution for Detecting Guns in School Environment using Deep Learning Techniques Olsson, Johan January 2019 (has links) Att använda maskininlärning för att detektera vapen eliminerar en konstant mänsklig övervakning, vilket också kan leda till en lägre responstid till polis. I den här rapporten undersöks hur en vapendetektor kan konstrueras och byggas som en del av en klient-server-lösning. / With the progress of deep learning methods the last couple of years, object detectionrelated tasks are improving rapidly. Using object detection for detecting guns in schoolsremove the need for human supervision and hopefully reduces police response time. Thispaper investigates how a gun detection system can be built by reading frames locally andusing a server for detection. The detector is based on a pre-trained SSD model and throughtransfer learning is taught to recognize guns. The detector obtained an Average Precisionof 51.1% and the server response time for a frame of size 1920 x 1080 was 480 ms, but couldbe scaled down to 240 x 135 to reach 210 ms, without affecting the accuracy. A non-gunclass was implemented to reduce the number of false positives and on a set of 300 imagescontaining 165 guns, the number of false positives dropped from 21 to 11. machine learning gun detection tensorflow server client training school shootings school environment gun control SSD RetinaNet transfer learning AI surveillance IP network camera Axis Communications Media and Communication Technology Medieteknik
3	A Client-Server Solution for Detecting Guns in School Environment using Deep Learning Techniques Olsson, Johan January 2019 (has links) With the progress of deep learning methods the last couple of years, object detection related tasks are improving rapidly. Using object detection for detecting guns in schools remove the need for human supervision and hopefully reduces police response time. This paper investigates how a gun detection system can be built by reading frames locally and using a server for detection. The detector is based on a pre-trained SSD model and through transfer learning is taught to recognize guns. The detector obtained an Average Precision of 51.1% and the server response time for a frame of size 1920 x 1080 was 480 ms, but could be scaled down to 240 x 135 to reach 210 ms, without affecting the accuracy. A non-gun class was implemented to reduce the number of false positives and on a set of 300 images containing 165 guns, the number of false positives dropped from 21 to 11. machine learning gun detection tensorflow server client training school shootings school environment gun control SSD RetinaNet transfer learning AI surveillance IP network camera Axis Communications Media and Communication Technology Medieteknik

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