Thermal anomaly detection based on saliency computation for district heating system

The leaked heat pipeline can be detected as temperature anomalies from the air-borne thermal image. Existing methods of thermal anomaly detection are prone to generate a large quantity of false alarms. Although supervised classification can reduce the false positive rate, it requires years of accumulated training data. In this paper, we use human visual system to improve the detection capabilities of thermal anomaly in district heating system. Leakage candidates are selected from the saliency map created by the thermal image, then buffer analysis with pipeline GIS layer is used to reject false detections. Experimental results show that the proposed method has better performance in detection rate when prior knowledge is scarce, and it is more adaptable to actual circumstances.