Abstract: In response to the challenge of effectively utilizing a massive amount of images in discarded household appliances recycling techniques,a discarded household appliance image recognition model,named ME-ResNet(multi-scale and efficient ResNet),is proposed based on ResNet and multi-scale convolution.Firstly,a multi-scale convolution module is designed using a residual structure to enhance the model's capability in extracting feature information across different scales.Building upon this,the ME-ResNet model is specifically designed for the classification of discarded household appliance images based on ResNet.Secondly,lightweighting of the ME-ResNet model is achieved by replacing certain convolutional layers in multi-scale convolution with depthwise separable convolution.Finally,the performance of ME-ResNet and its lightweight variant are validated through comparative experiments with other convolutional neural networks.Research results demonstrate that both ME-ResNet and its lightweight model effectively improve recognition accuracy.Compared to the classical convolutional neural network ResNet34,ME-ResNet and its lightweight version achieve respective optimal accuracy increases of 1.2% and 0.3%,macro-precision increases of 1.7% and 0.9%,macro-recall increases of 1.3% and 0.2%,and macro-F1 score increases of 1.5% and 0.5%,respectively.

Key words: Multi-scale convolution, ME-ResNet model, Depthwise separable convolution, Image classification, Residual connection