A Cross-Domain Milk Freshness Detection Method Based on Transfer Learning

As an important and widely studied research topic, the detection of fresh milk plays a crucial role in protecting consumer health. However, due to the diversity of milk brands and environmental conditions in real-world application scenarios, the accuracy of existing detection models often drops significantly when applied to new domains. A significant amount of new data needs to be gathered in order to retrain the model for new domain, which greatly increases both time and labor costs. To tackle this problem, this paper introduces a transfer learning-based cross-domain milk freshness detection method. The method uses transfer learning to generate data for unknown categories within the target domain. Specifically, the transfer generation model trains on data from both the target and source domain categories. With the proposed model, data from other categories in the source domain can be utilized to generate corresponding target domain data. This method comprehensively considers data generation from multiple perspectives of time, frequency, and spatial, to enhance the authenticity of the generated data. It uses a transfer generation architecture, consisting of a transfer network and a decoder, to learn the mapping relationship between the source and target domain, helping to narrow the gap between domains. Additionally, a feature subspace decomposition method is introduced into the decoder, and a cross-domain consistency loss function is formulated to strengthen the model’s learning capability. The proposed method is shown to generate high-quality data for unknown target-domain categories in multiple cross-domain settings, leading to a performance improvement ranging from 17.64% to 32.44% in the detection of cross-domain milk freshness.