Abstract
High-frequency workpieces have the characteristics of complex intra-class textures and small differences between classes, leading to the problem of low recognition rates when existing models are applied to the recognition of high-frequency workpiece images. We propose in this paper a novel high-frequency workpiece image recognition model that uses EfficientNet-B1 as the basic network and integrates multi-level network structures, designated as ML-EfficientNet-B1. Specifically, a lightweight mixed attention module is first introduced to extract global workpiece image features with strong illumination robustness, and the global recognition results are obtained through the backbone network. Then, the weakly supervised area detection module is used to locate the locally important areas of the workpiece and is introduced into the branch network to obtain local recognition results. Finally, the global and local recognition results are combined in the branch fusion module to achieve the final recognition of high-frequency workpiece images. Experimental results show that compared with various image recognition models, the proposed ML-EfficientNet-B1 model has stronger adaptability to illumination changes, significantly improves the performance of high-frequency workpiece recognition, and the recognition accuracy reaches 98.3%.