## 1 Introduction  
With the rapid development of the industrial internet, industrial software has become an integral component of industrial production. However, due to the complexity and diversity of industrial software, its testing process often necessitates significant manpower and resources. In order to enhance testing efficiency and accuracy, automated testing technology has emerged. Automated testing allows for continuous testing throughout the software development process, facilitating prompt identification and resolution of issues, thus mitigating software development costs and risks. Utilitarian tools such as Monkey [1; 2] and Dynodroid [3] have gained widespread industrial application as practical automated testing instruments. Yet, these automated testing tools primarily concentrate on detecting crash issues rather than UI display problems. As an automated testing approach, GUI automation testing has been extensively implemented in software testing to boost efficiency, reduce costs, and improve quality. Central to GUI automation testing is the object recognition technology for target objects, which includes various controls within the GUI interface, such as buttons, text fields, and drop-down menus. Consequently, the accuracy and stability of object recognition technology bear the utmost significance for the effectiveness and reliability of automated testing.  
In recent years, the rapid progress in artificial intelligence technology has given rise to innovative ideas and methodologies aimed at improving GUI testing. This involves leveraging deep learning for the automatic recognition of GUI elements in screen captures. The recognition of GUI elements within images, based on pixels, can be viewed as a specialized object detection task within the domain of GUI testing. Object detection, a computer vision technology, facilitates the identification of specific semantic objects within digital images and videos.  
This paper presents a target detection algorithm based on an improved Faster R-CNN. Initially, ResNet-50 is adopted as the feature extraction network, with the incorporation of enhanced multi-scale attention (EMA) within ResNet-50 to bolster attention to relevant channel information across both channel and spatial dimensions, thereby augmenting performance. Feature pyramid network (FPN) is introduced to bolster the algorithm's capability of handling small targets, resulting in improved robustness and efficiency during the learning process. ROI-Align supersedes ROI-Pooling for target feature mapping, effectively addressing false alarms and missed detections caused by limited model generalization and ultimately enhancing detection accuracy. Additionally, a training data generation algorithm has been devised, which automatically generates datasets with GUI display problems based on the Rico dataset.  
The structure of this paper is as follows. Section 2 explores the existing research in the field of GUI-related studies. Section 3 provides a comprehensive exposition of the training data generation algorithm based on the Rico dataset, the improved Faster R-CNN, and their respective components. Section 4 describes the experimental setup and preparation, as well as the analysis of experimental results. Finally, in Section 5, this paper presents the conclusion and discusses future work.