A Hybrid Retina Net Classifier for Thermal Imaging

Abstract

Thermal imaging is a cutting-edge technology which has the capability to detect objects in any environmental conditions, such as smoke, fog, smog, etc. This technology finds its importance mainly during nighttime since it does not require light to detect the objects. Applications of this technology span into various sectors, most importantly in border security to detect any incoming hazards. Object detection and classification are generally difficult with thermal imaging. In this paper, a one-stage deep convolution network-based object detection and classification called retina net is introduced. Existing surveys are based on object detection using infrared information obtained from the objects. This research is focused on detecting and identifying objects from thermal images and surveillance data.

Evaluation of Preferences for a Thermal-Camera-Based Abnormal Situation Detection Service via the Integrated Fuzzy AHP/TOPSIS Model

“…In the field of fire research, one study was conducted to detect flashover phenomena involving rapid fire propagation using IR image sequences [22]. Studies have explored HW and system development for effective day and night surveillance by continuously using thermal cameras [17,[23][24][25][26]. Others have examined human face recognition based on recent advanced thermography images [27], recognition of abnormal behaviors [28,29], and falling recognition [30,31].…”