A Non-Intrusive Automated Testing System for Internet of Vehicles App Based on Deep Learning

Yanan Zhang, Zhen Guo, Tao Sun

In the non-intrusive automated testing system for Internet of Vehicles (IoV) applications, automatic recognition of text and icons on vehicle central control screens is of paramount importance. However, the detection and recognition of content on vehicle central control screens are inherently complex. Additionally, during non-intrusive vehicle central control screen image testing, there is a deficiency of suitable datasets and detection methods. This deficiency renders information within vehicle application images difficult to be accurately extracted by the detection network. To address this problem, this study first constructs a dataset tailored for text detection and recognition on vehicle screens. This dataset encompasses a variety of vehicle central control images, enabling the generic text detection and recognition network to more effectively identify and interpret text within vehicle screens. Subsequently, this research proposes an enhanced Fully Convolutional Networks for Text Detection (FOTS) method for vehicle central control screen text detection and recognition. This method elevates the semantic expression capabilities of features by sharing vehicle central control screen text detection and recognition features. Furthermore, it improves multi-scale feature processing capabilities through the utilization of a feature transformation module. Validation through visual and quantitative experiments demonstrates that the proposed method can effectively accomplish text detection and recognition tasks on vehicle screens. This achievement bears significant implications for the field of automated testing in IoV applications.