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Detection of weapons using Efficient Net and Yolo v3

Abstract:

With only 9% of the world's population, Latin America has one of the highest rates of violence in the world, generating insecurity, crime, robberies, weapons and homicides. In this project we worked with object detection to detect various types of weapons in public spaces such as stores, ATMs, streets, among others. Several trainings with different data sets and different neural network models were evaluated on the plataform Google colaboraty. Two models were used for training, Yolo v3 and Efficient D0, the models were trained with four categories of firearms; pistol, submachine gun, shotgun and rifle. The results of the experiments show that Yolo v3 is the best network for detecting firearms with an accuracy of 0.80 out of 1.