A Novel Fusion Pruning-Processed Lightweight CNN for Local Object Recognition on Resource-Constrained Devices

In recent years, smartphones and smart educational products have become important catalysts for the development of consumer electronics. However, deploying large convolutional neural networks (CNNs) models on resource-constrained devices like smartphones is impractical due to the lack of high-performance central processing units (CPUs), graphics processing units (GPUs), and large-capacity memory. To address these challenges, we propose a new Fusion Pruning (FP) method aimed at compressing and accelerating large CNN models by leveraging L2 norm and equivalent transformation of the receptive field. To validate the feasibility of the proposed method in language education, we have developed an application deployed on smartphones. This application enables offline object recognition without the need for additional hardware platforms like Jetson Xavier NX. Additionally, we have also explored the performance of the FP method in the field of household robotics, obtaining satisfactory results. Experimental results demonstrate that the proposed method can accomplish tasks on resource-constrained devices.