Surveillance cameras are a great support in crime investigation and proximity alarms and play a v... more 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
International Journal of Artificial Intelligence & Applications
Firearm Shootings and stabbings attacks are intense and result in severe trauma and threat to pub... more Firearm Shootings and stabbings attacks are intense and result in severe trauma and threat to public safety. Technology is needed to prevent lone-wolf attacks without human supervision. Hence designing an automatic weapon detection using deep learning, is an optimized solution to localize and detect the presence of weapon objects using Neural Networks. This research focuses on both unified and II-stage object detectors whose resultant model not only detects the presence of weapons but also classifies with respective to its weapon classes, including handgun, knife, revolver, and rifle, along with person detection. This research focuses on YOLOv5 (You Look Only Once) family and Faster RCNN family for model validation and training. Pruning and Ensembling techniques were applied to YOLOv5 to enhance their speed and performance. YOLOv5 models achieve the highest score of 78% with an inference speed of 8.1ms. However, Faster R-CNN models achieve the highest AP 89%.
Surveillance cameras are a great support in crime investigation and proximity alarms and play a v... more 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
Surveillance cameras are a great support in crime investigation and proximity alarms and play a v... more 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
International Journal of Artificial Intelligence & Applications
Firearm Shootings and stabbings attacks are intense and result in severe trauma and threat to pub... more Firearm Shootings and stabbings attacks are intense and result in severe trauma and threat to public safety. Technology is needed to prevent lone-wolf attacks without human supervision. Hence designing an automatic weapon detection using deep learning, is an optimized solution to localize and detect the presence of weapon objects using Neural Networks. This research focuses on both unified and II-stage object detectors whose resultant model not only detects the presence of weapons but also classifies with respective to its weapon classes, including handgun, knife, revolver, and rifle, along with person detection. This research focuses on YOLOv5 (You Look Only Once) family and Faster RCNN family for model validation and training. Pruning and Ensembling techniques were applied to YOLOv5 to enhance their speed and performance. YOLOv5 models achieve the highest score of 78% with an inference speed of 8.1ms. However, Faster R-CNN models achieve the highest AP 89%.
Surveillance cameras are a great support in crime investigation and proximity alarms and play a v... more 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
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Papers by Akhila Kambhatla