Application of YOLOX deep learning model for automated object detection on thermograms

Abstract

A method of automating the data analysis of thermal imaging systems in the field of safety control is proposed. It has been established that today video surveillance technologies have a number of disadvantages that can be eliminated by using thermal imaging cameras. Analysis of infrared images can be automated in order to reduce percentage of false positives and increase the efficiency of thermal imaging video surveillance systems. A high level of interference, unclear object contours and low image resolution are real problems in automating the object detecting process on thermographic images. The traditional and promising methods of thermograms analysis and approaches that can lead to creating the automated thermal video surveillance systems are discussed. It is proposed to use deep learning, which in recent years has proven itself as an effective way of image analysis. The study is based on review of existing works, as methods of automating object detection process on thermograms. It is proposed to use YOLOX as a deep learning model. This model has one of the best quality indicators and speed processing input parameters on standard datasets. FLIR’s Thermal Starter annotated set of thermal images is used to train the model. The value of mAP at the level 55% is obtained according the results of model training for recognizing 4 classes of objects on thermograms. Different advantages and disadvantages of this development are analyzed. Ways of further improvement of the neural network method of automation of thermal imaging safety control systems have been determined.