Abstract
Today, with the increasing number of criminal activities, automatic control systems are becoming the primary need for security forces. In this study, a new model is proposed to detect seven different weapon types using the deep learning method. This model offers a new approach to weapon classification based on the VGGNet architecture. The model is taught how to recognize assault rifles, bazookas, grenades, hunting rifles, knives, pistols, and revolvers. The proposed model is developed using the Keras library on the TensorFlow base. A new model is used to determine the method required to train, create layers, implement the training process, save training in the computer environment, determine the success rate of the training, and test the trained model. In order to train the model network proposed in this study, a new dataset consisting of seven different weapon types is constructed. Using this dataset, the proposed model is compared with the VGG-16, ResNet-50, and ResNet-101 models to determine which provides the best classification results. As a result of the comparison, the proposed model’s success accuracy of 98.40% is shown to be higher than the VGG-16 model with 89.75% success accuracy, the ResNet-50 model with 93.70% success accuracy, and the ResNet-101 model with 83.33% success accuracy.
Highlights
In the age of modern science and technology, people use surveillance cameras in different areas to prevent crime [1]
The model we proposed was preferred for the problem of weapon detection and recognition due to the small number of layers compared to the classic VGGNet model, the ability to train on low-cost computers, the low training time, and the high success accuracy
Experiments were conducted for seven different weapon types and the proposed model was compared with the VGG-16, ResNet-50, and ResNet-101 models
Summary
In the age of modern science and technology, people use surveillance cameras in different areas to prevent crime [1]. After a crime occurs, security guards arrive at the scene, after checking the recorded images they analyze the images and collect the necessary evidence [4]; it is necessary to establish a proactive system at the crime scene [3,5,6]. In this context, if the software alerts the security guards immediately after detecting threatening objects, prompt action can be taken to stop the potential criminal from committing a crime [4,7]. CNNs have achieved the best results for classical image processing problems, such as image segmentation, classification, and detection [15,16]
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