Recognition of Explosive Objects Using Computer Vision and Machine Learning

This article considers an approach to the recognition of explosive objects using a custom object detection model with Tensorflow framework and OpenCV. The approach to creating a customer's own SSD model is considered in detail. Analyzed the benefits of using OpenCV to deploy an explosive object detection system. Briefly describe the application for testing and visualizing the work of the resulting model. The purpose of the research is using machine learning and computer vision as a new approach for resolving problem of detecting explosive objects. The object of research - the process of detecting explosive objects. Methods of research - methods of object detection, methods of machine learning, methods of simulation.