Improved Lightweight Deep Learning Model for the Fine-Feature Classification of Pipe Components
Keywords:
Object classification, Subtle image features, Lightweight network, IndustrialAbstract
In the field of industrial manufacturing, the most common application of machine vision is object classification. Traditional models face challenges in classifying objects with small features and have yet to develop a complete universal algorithm, resulting in lower accuracy. To address these issues and enable the model's use on portable embedded devices, as well as facilitate the classification of pipe components with subtle internal features, a lightweight deep learning model has been developed. This model utilizes a relatively small dataset comprising specific types of work pieces occurring in actual factory production. The dataset involves the use of subtle image features for the classification of pipe components. The proposed model combines a fine-tuning module to capture multi-scale features of the input image and utilizes attention mechanisms to enhance the model's generalization ability. Not only enhances detection accuracy but also achieves network lightweight, with an accuracy of 96.33%. A comparison with other models demonstrates an improvement in accuracy of at least 4%, along with a significant reduction in both total and training parameters, meeting usability requirements, possessing lower computational complexity is essential to ensure fast and efficient operation in scenarios with embedded devices, mobile devices, or other resource-constrained environments.
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