In allusion to the problem that the existing can defect detection system has high false detection rate and missed detection rate and relatively low detection accuracy in high-speed production lines, this paper aims to propose an algorithm for online detection of can manufacturing based on deep learning and transfer learning techniques to improve the accuracy of can defect identification and make the can production line more automated and intelligent. This paper used deep convolutional network to extract the characteristics of can defects, and optimized the convolution kernel to reduce the time required for can defect detection. Due to the lack of domestic and foreign data sets for defective images of food packaging manufacturing, a can defect data set was built, and the accuracy of identifying can defects was improved by adjusting VGG16 in combination with pre-trained network. The performance comparison for can test was performed on the convolution neural network, transfer learning, and adjusted pre-trained network. It was verified that this technology had a good recognition effect when the learning rate was 0.0005 and the epochs of trainings was 10. The recognition rate of the final binary classification was 99.7% and the time consumption of algorithm was 119 ms. Compared with the existing can detection algorithms, this paper proposes a deep learning-based can detection algorithm with better recognition performance and higher intelligence. At the same time, this study helps enterprises to use AI technology such as deep learning to promote intelligent production, reduce human cost, and conform to the strategy of national manufacturing industry upgrading, which has certain practical significance.