A high efficiency and high precision testing method for tightness detection method of aluminum foil seal is proposed.First of all, the thermal images of aluminum foil seal are obtained by a thermal imager.Then we use MATLAB software for getting the information of thermal image in batches,and then the image preprocessing. Then BP neural network was optimized by genetic algorithm to extract and classify image features.The experimental results show that this sealing test method has high recognition rate and short training time, which provides a guarantee for automatic screening and elimination of bad sealing products in the subsequent aluminum foil sealing production line.

A method for detecting the integrity of aluminum foil seals with high detection accuracy and high detection efficiency is proposed.First of all,in order to obtain the infrared image of the sealing part aluminum foil,the method has adopted the infrared imaging technology.Then，the infrared image is preprocessed and the feature is extracted by circular symmetric Gabor wavelet.Lastly,KNN is used to identify and classify the acquired imaging.Experiments show that the circular symmetric Gabor wavelet used by this sealing detection method has higher recognition rate and faster algorithm than the traditional Gabor transform algorithm, and provides technical assurance for the automatic screening and rejection of defective sealing products in the subsequent aluminum foil sealing production line.