@article{Chen2023, 
author = {Jing-yu Chen and Yi Lyu},
title = {Frost Detection Method of Cold Chain Refrigerating Machine Based on Spiking Neural Network},
year = {2023},
journal = {Journal of Guangdong University of Technology},
volume = {40},
number = {1},
pages = {29-38},
keywords = {spiking neural network, frost detection, dynamic region detection, refrigerating machine image},
url = {https://www.sciopen.com/article/10.12052/gdutxb.220120},
doi = {10.12052/gdutxb.220120},
abstract = {The traditional frost detection method based on image processing technology is difficult to flexibly and accurately detect the cold chain refrigeration unit in complex production environment. It is also easy to misjudge due to the influence of environmental factors such as illumination and fog. Therefore, a cold chain refrigerating machine frosting detection method based on spiking neural network is designed. Taking the refrigerator image as the input, the dynamic change of the frosting area of the refrigerator evaporator is automatically detected, the abnormalities caused by interference factors such as illumination and fog are corrected, and the double threshold divided by the cumulative value of pulse emission rate is used as the judgment basis of frosting degree. Experiments are carried out on several cold chain refrigerators put into the production environment. The results show that the designed spiking neural network can adaptively detect the dynamic region of the frosting area of the refrigerator evaporator under the actual production environment and the double threshold which is divided accurately can judge the frosting degree of the evaporator. The detection effect is good and the stability is strong, which can provide a reliable basis for the defrosting time of the defrosting strategy of the cold chain refrigeration unit.}
}