High-Frequency Workpiece Image Recognition Model Based on Hybrid Attention Mechanism
Source abstract
Abstract
High-frequency workpieces are specialized items characterized by complex internal textures and minimal variance in properties. Under intricate lighting conditions, existing mainstream image recognition models struggle with low precision when applied to the identification of high-frequency workpiece images. This paper introduces a high-frequency workpiece image recognition model based on a hybrid attention mechanism, HAEN. Initially, the high-frequency workpiece dataset is enhanced through geometric transformations, random noise, and random lighting adjustments to augment the model’s generalization capabilities. Subsequently, lightweight convolution, including one-dimensional and dilated convolutions, is employed to enhance convolutional attention and reduce the model’s parameter count, extracting original image features with robustness to strong lighting and mitigating the impact of lighting conditions on recognition outcomes. Finally, lightweight re-estimation attention modules are integrated at various model levels to reassess spatial information in feature maps and enhance the model’s representation of depth channel features. Experimental results demonstrate that the proposed model effectively extracts features from high-frequency workpiece images under complex lighting, outperforming existing models in image classification tasks with a precision of 97.23%.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Advanced Neural Network Applications · Image and Object Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Southwest Institute of Electronic Technology, Chengdu 610036, China
Funding
Key R&D Project of Sichuan Province Science and Technology Department
References (38)
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