Detection of dangerous objects in images using neural networks

Abstract

In recent years, along with the continuous improvement of the computer speed, the application of the computer for the examination and recognition of dangerous goods has become more and more widespread. In order to overcome the shortcomings of the high frequency of false detection in the process of classifying targets using existing feature-based classifiers, this paper proposes an algorithm for detecting dangerous objects using a convolutional neural network based on deep learning. For the image being checked, sliding windows of different scales are used to determine the presence of an object window. To detect objects, a convolutional neural network is trained with a large number of positive and negative samples. For better adaptation to object detection, the topology of the convolutional neural network has been improved. A window of suspected dangerous object is fed into an improved convolutional neural network to detect dangerous objects, and the false detection rate is reduced while maintaining the original detection rate.