Efficient Deep Learning Modalities for Object Detection from Infrared Images

Abstract

For military warfare purposes, it is necessary to identify the type of a certain weapon through video stream tracking based on infrared (IR) video frames. Computer vision is a visual search trend that is used to identify objects in images or video frames. For military applications, drones take a main role in surveillance tasks, but they cannot be confident for long-time missions. So, there is a need for such a system, which provides a continuous surveillance task to support the drone mission. Such a system can be called a Hybrid Surveillance System (HSS). This system is based on a distributed network of wireless sensors for continuous surveillance. In addition, it includes one or more drones to make short-time missions, if the sensors detect a suspicious event. This paper presents a digital solution to identify certain types of concealed weapons in surveillance applications based on Convolutional Neural Networks (CNNs) and Convolutional Long Short-Term Memory (ConvLSTM). Based on initial results, the importance of video frame enhancement is obvious to improve the visibility of objects in video streams. The accuracy of the proposed methods reach 99%, which reflects the effectiveness of the presented solution. In addition, the experimental results prove that the proposed methods provide superior performance compared to traditional ones.