Improved DCGAN Data Augmentation for Household Garbage Image Recognition
To address the issues of low image quality and uneven class distribution in the current field of household waste classification datas-ets,a garbage image generation method based on improved DCGAN data augmentation(EW-DCGAN)is proposed.Firstly,redesign the net-work structure of DCGAN and adjust the size of the output image of the generator to 128×128 pixels;Then,the loss function BCE Loss is re-placed with a loss function with Wasserstein distance,and a gradient penalty term is introduced to enhance the discriminative ability of the model discriminator;Finally,the ECA attention mechanism is added to the model generator to better cope with the interference of invalid in-formation in the image,thereby efficiently extracting useful features.The experiment shows that the image quality generated using EW-DC-GAN is higher,and the FID value decreases significantly compared to images generated only using DCGAN.It can expand and enhance the da-taset in the field of garbage classification.The comparison of ResNet,MobileNet,and EfficientNet neural networks based on transfer learning on the pre enhanced and post enhanced datasets showed that the accuracy of the models improved by 7.09%,5.34%,and 4.8%,respectively,compared to the original dataset.
deep convolutional generative adversarial networkgarbage classificationdata augmentationWasserstein distance
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