Lightweight Image Classification Algorithm Based on Domain Generalization

To address the lack of Sleeping on Duty datasets, poor generalization of current classification algorithms, and slow inference speeds, a Sleeping on Duty dataset containing 4 708 images is constructed to verify the recognition accuracy and generalization ability of the model. Additionally, a lightweight image classification algorithm, Stable_MobileNet, based on domain generalization, is proposed. First, the input images are padded along the shorter edges to maintain the aspect ratio of people within the images, followed by image enhancement and random erasure to expand the dataset. Second, the Efficient Channel Attention (ECA) module is introduced to improve the MobileNetv3_large network. Finally, the stable learning method, StableNet, is applied to enhance the generalization of the model by learning the weights of the training samples, reducing feature dependency, and allowing the model to focus more on character features rather than environmental factors. Experimental results on the Sleeping on Duty dataset indicate that Stable_MobileNet achieves faster average inference compared to MobileNetv3_large, with a recognition accuracy of 93.56%, which is 2.23% higher than that of MobileNetv3_large. In the test set, where the sample distribution differed from that of the training set, the recognition accuracy of Stable_MobileNet is improved by 2.23%.