Fruit classification using attention-based MobileNetV2 for industrial applications.

ABSTRACT: Recent deep learning methods for fruits classification resulted in promising performance. However, these methods are with heavy-weight architectures in nature, and hence require a higher storage and expensive training operations due to feeding a large number of training parameters. There is a necessity to explore lightweight deep learning models without compromising the classification accuracy. In this paper, we propose a lightweight deep learning model using the pre-trained MobileNetV2 model and attention module. First, the convolution features are extracted to capture the high-level object-based information. Second, an attention module is used to capture the interesting semantic information. The convolution and attention modules are then combined together to fuse both the high-level obje

SUBMITTER: Shahi TB 

PROVIDER: S-EPMC8880666 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature