Surveillance cameras are a great support in crime investigation and proximity alarms and play a vital role in public safety. However current surveillance systems require continuous human supervision for monitoring. The primary goal of the thesis is to prevent firearm-related violence and injuries. Automatic firearm detection enhances security and safety among people. Therefore, introducing a Deep Learning Object Detection model to detect Firearms and alert the corresponding police department is the main motivation. Visual Object Detection is a fundamental recognition problem in computer vision that aims to find objects of certain target classes with precise localization of input image and assign it to the corresponding label. However, there are some challenges arising to the wide variations in shape, size, appearance, and occlusions by the weapon carrier. There are other objections to the selection of best object detection model. So, three deep learning models are selected, explained and shown the differences in detecting the firearms. The dataset in this thesis is the customized selection of different categories of firearm collection like the pistol, revolver, handgun, bullet, rifle along with human detection. The entire dataset is annotated manually in pascalvoc format. Date augmentation technique has been used to enlarge our dataset and facilitate in detecting firearms that re deformed and having occlusion properties.. To detect firearms this thesis developed and practiced unified networks like SSD and two-stage object detectors like faster RCNN. SSD is easy to understand and detect objects however it fails to detect smaller objects. Faster RCNN are efficient and able to detect smaller firearms in the scene. Each class has attained more than 90% of confidence score.
| Identifer | oai:union.ndltd.org:siu.edu/oai:opensiuc.lib.siu.edu:theses-3787 |
| Date | 01 May 2020 |
| Creators | Kambhatla, Akhila |
| Publisher | OpenSIUC |
| Source Sets | Southern Illinois University Carbondale |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses |
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