Lightweight Recognition Method Based on Edge Computing
The emergence of the"new retail"model has significant implications in transforming the traditional retail industry and providing enhanced consumer experience.However,existing unmanned fruit weighing scales in the market still face several challenges,such as low recognition rates,complex model structures,deployment difficulties,and poor real-time inference capabilities.To address these issues,this study proposes a lightweight recognition method based on edge computing.First,MobileNext was selected as the backbone network.Second,the Convolutional Block Attention Module(CBAM),was introduced,as a lightweight attention module,to improve the SandGlass module in MobileNext.Subsequently,the Ghost module was utilized to replace the standard 1×1 convolution in the SandGlass module,thereby reducing the number of model parameters and computational complexity.Finally,the improved MobileNext model was trained using a transfer learning strategy combined with Nesterov-accelerated Adaptive moment estimation(NAdam)optimizer to further enhance recognition accuracy.In experiments on the Fruit Recognition dataset,the proposed recognition method achieved a recognition accuracy of 98.95％,outperforming lightweight models,such as the original MobileNext model,MobileNetV2,and EfficientNet-B0.Compared to the original MobileNext model,the improved MobileNext model achieved a 1.17 percentage points increase in recognition accuracy with only 1.775 million parameters and an inference time of only 16.5 ms.In practical retail scenarios,this method requires minimal parameters and computational resources to achieve satisfactory recognition performance and has been successfully deployed on edge devices.
deep learningattention modulelightweight modeltransfer learningedge devices
万方数据
维普
