# High-Frequency Workpiece Image Recognition Model Based on Hybrid Attention Mechanism
## 3 Experiments
### Recognition Precision Comparison Experiment  
In order to verify the recognition effect of the model proposed in this paper, the HAEN model was compared with the original network EfficientNet-B0 and the recent recognition model related to solving complex lighting problems on the high-frequency workpiece dataset. The comparison models included the following: (1) the basic model was EfficientNet-B0, denoted as Basis; (2) the recognition model that integrated the gradient feature [30], denoted as IGFN (Integrated Gradient Features Network); (3) the model based on the attention mechanism [31], denoted as IAN (Improved AlexNet For Fish Species Recognition); (4) the model based on Retinex and the attention mechanism [32], denoted as IRN (Improved RegNet For Traffic Sign Recognition). In order to accurately test the difference in the effects of different models, the experimental environment and experimental dataset in all model experiments were consistent with Sections 3.1 and 3.2, respectively. The comparison results of the high-frequency workpiece recognition of different models are shown in Table 2.  
The following can be seen from Table 3: (1) When directly using the EfficientNet-B0 network to classify high-frequency workpiece images, the recognition precision was 87.92%, which was the worst among all models and much lower than IGFN, IAN, and IRN. (2) Compared with IGFN, IAN, and IRN, the recognition precision of the HAEN model was higher by 5.45%, 6.38%, and 4.59% respectively, indicating that the model proposed in this article has significant advantages in the precision of high-frequency workpiece recognition. (3) In the HAEN model, the highest precision rate of 97.23% was achieved on the high-frequency workpiece dataset, indicating that the data enhancement and hybrid attention mechanisms have high recognition precision. (4) The HAEN model outperformed \begin{table}
\begin{tabular}{c c c} \hline \hline
**H\({}^{\prime}\),W\({}^{\prime}\) Values** & **Precision/\%** & **Parameters/M** \\ \hline \(\left<7,7\right>\) & 97.23 & 6.05 \\ \(\left<5,5\right>\) & 96.93 & 5.69 \\ \(\left<3,3\right>\) & 96.74 & 5.45 \\ \(\left<1,1\right>\) & 96.41 & 5.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The influence of H\({}^{\prime}\) and W\({}^{\prime}\) on the recognition results of high-frequency workpieces. IAN and IRN in terms of parameter count and surpassed IGNN and IAN in training time. Overall, the HAEN model is superior to other models according to various metrics. In order to further analyze the recognition effects of different methods, Basis and the HAEN model were compared by using a confusion matrix, and the comparison results are shown in Figure 11. It can be intuitively seen from the figure that the HAEN model showed a significant improvement in the classification precision of high-frequency workpieces compared with Basis. From the confusion matrix, it can be seen that the classification results of the HAEN model are more concentrated on the diagonal, while the proportion of misclassification on the non-diagonal line is significantly reduced. The classification precision of each category of workpieces was above 0.90, which shows that through data enhancement and the introduction of the ILCBAM and LRAM, the influence of illumination changes on the classification results was overcome and the perception ability of Basis for strong illumination-robust feature information was improved, which could effectively solve the problem of high-frequency workpieces being difficult to accurately classify due to illumination changes. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Backbone** & **Parameter** & **Training** & **Precision/\%** \\  & **Network** & **Quantity/M** & **Time/s** & **Precision/\%** \\ \hline Basis & EfficientNet-B0 & 5.30 & 2314 & 87.92 \\ IGFN & SqueezeNet & 5.43 & 2563 & 91.78 \\ IAN & AlexNet & 240.17 & 4620 & 90.85 \\ IRN & RegNet & 25.19 & 2149 & 92.64 \\ HAEN & EfficientNet-B0 & 6.05 & 2405 & 97.23 \\ \hline \hline \end{tabular}
\end{table}