Enhancing Vehicle-Machine Interface Icon Detection in Automated Testing Through a Three-Stage Machine Vision Verification Algorithm

Abstract

In the context of automated testing in the automotive industry, the accurate and effective detection of target icons on vehicle-machine interfaces is crucial for ensuring the efficiency and precision of automated testing processes. This paper addressed the challenge of effectively detecting vehicle-machine interface icons by proposing a three-stage verification detection algorithm based on machine vision. This paper employed an enhanced template matching technique to achieve precise positioning and initial verification of car and machine icons. Subsequently, a two-stage verification process was implemented, which focuses on detecting local changes using pixel blocks. The three-stage verification was accomplished through a three-channel separation detection of characteristic pixel points based on RGB values. Additionally, this paper utilized the collaborative capabilities of LabVIEW and OpenCV to overcome integration challenges. This joint programming approach bridged the gap between LabVIEW and OpenCV. Experimental results unequivocally underscored the paramount significance of the three-stage verification detection algorithm, rooted in the realm of machine vision. The algorithm’s novel three-stage approach, intricately designed to combat background interference, not only elevated the precision of icon detection but also introduced an ingenious mechanism for classifying detection results. In this regard, it successfully overcome the limitations that have historically hindered traditional machine vision algorithms, ultimately demonstrating its paramount importance in the field of computer vision.

Keywords

• Vehicle-machine interface
• machine vision
• three-stage verification
• pixel block