A centralized frost detection and estimation scheme for Internet-connected domestic refrigerators

Frost accumulation on heat exchange units is a significant problem in refrigeration systems, adversely affecting their operating performance and thereby leading to increased power consumption. Therefore, timely detection and accurate quantification of frost are crucial for effective defrosting strategies. This study presents a novel centralized cloud-based IoT scheme for frost detection and thickness estimation. The image processing is performed on the cloud server to process evaporator coil images for frost thickness quantification. Experiments were conducted on a domestic refrigerator to assess the effectiveness of the proposed image-processing approach and determine latency and processing time. The presented scheme effectively quantifies frost thickness on the evaporator in the 1–5 mm range with a 10.8% error margin. The total inference time, which includes image acquisition, pre-processing, transmission latency, and frost thickness estimation, is approximately 5.15 seconds. The results demonstrate that the proposed image processing method performs comparably to conventional sensors and similar image processing techniques. Moreover, the centralized cloud-based IoT architecture presented effectively meets the scalability demands of consumer refrigerators.