## 5 Experiments
### Experiment Result Analysis  
To ensure a fair experimental comparison, we adopt the method of controlling variables. All aspects, aside from the variation in networks, encompassing the dataset, training steps, and parameter configurations, remain consistent. Throughout the experimental process, the datasets containing GUI display issues are partitioned into proportions of 8:1:1, as previously described. Models undergo training utilizing pre-trained models from the COCO2017 dataset, with 20 training iterations, an initial learning rate of 0.005, and a batch size of 8; the optimal weight parameters are selected based on the training results. In Experiment 1, improvements are sequentially implemented in the original Faster R-CNN network. Specifically, VGG16 is replaced with ResNet-50 as the feature extraction network. A feature pyramid network (FPN) and multi-scale attention (EMA mechanism are incorporated, and the more accurate ROI-Align replaces ROI-Pooling. Figure 11 illustrates a bar chart depicting the iterative enhancements in Faster R-CNN. Experiment names are abbreviated using capital letters, where R represents ResNet-50, F represents the feature pyramid network (FPN), E represents the enhanced multi-scale attention (EMA) mechanism, and A represents ROI-Align. Notably, each improvement experiment demonstrates advancements. Compared to the original Faster R-CNN algorithm, the final improved algorithm achieves notable enhancements in text overlap (0.094), image missing (0.028), component occlusion (0.126), and empty value (0.031). The final detection effectiveness reaches 0.784 (text overlap), 0.964 (image missing), 0.812 (component occlusion), and 0.930 (empty value). As observed in Table 2, the Faster R-CNN algorithm, incorporating all the enhancements, demonstrates a 7% increase in mAP compared to the original Faster R-CNN algorithm. Moreover, there is a notable surge in the AP value for the detection of each GUI display problem. In Experiment 2, the enhanced Faster R-CNN algorithm undergoes a comparative analysis with alternative CNN-based object detection algorithms. These algorithms are trained and evaluated using the identical dataset, and the ensuing comparison results are depicted in Table 3. Notably, the improved Faster R-CNN algorithm attains superior detection accuracy when juxtaposed with other CNN-based object detection algorithms. \begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Text Overlap** & **Image Missing** & **Component Occlusion** & **Empty Values** \\ \hline Training Set & 8000 & 8000 & 8000 & 8000 \\ Validation Set & 1000 & 1000 & 1000 & 1000 \\ Test Set & 1000 & 1000 & 1000 & 1000 \\ Sum & 10,000 & 10,000 & 10,000 & 10,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: GUI displays a problematic dataset. Discussions  
The experimental approach in this research leveraged the enhanced multi-scale attention (EMA) mechanism to enhance the Faster R-CNN object detection algorithm. Specifically, the EMA was incorporated into each convolutional layer of the residual networks within the feature extraction network, ResNet-50. This addition occurred after the output of the bn3 layer and before the application of the ReLU function. This strategic placement ensured that the structure of each residual block in ResNet-50 remained unchanged, enabling the use of pre-trained models. The motivation behind employing EMA lies in its strong generalization and computational capabilities, coupled with its flexibility and lightweight nature. The experimental results presented in Table 2 clearly demonstrate a significant improvement in mean average precision (mAP) for the Faster R-CNN algorithm with the inclusion of EMA. Specifically, there is a 7% enhancement compared to the original Faster R-CNN algorithm. When compared to a variant of Faster R-CNN that only replaces ROI-Align, the version incorporating EMA exhibits a 1.7% mAP increase, showcasing a distinct advantage in accuracy.