Optimizing 2D-to-3D image conversion for precise flat surface detection using laser triangulation and HSV masking

This study tackles a critical challenge in converting two-dimensional (2D) images into three-dimensional (3D) representations, focusing on the precise detection of flat surfaces. The research utilizes a triangulation method involving laser and camera systems, emphasizing the optimization of laser shooting angles and camera positioning to accurately determine z-coordinates. The methodology employs hue, saturation, and value (HSV) color masking, which has proven superior to traditional red, green, blue (RGB) methods for isolating red line objects. Key findings indicate that the optimal laser angle, β1=70.65°, significantly minimizes root mean square (RMS) error, thereby enhancing the accuracy of 3D imaging. Additionally, the use of three laser lines at different angles enables a more comprehensive detection of z-coordinates by creating multiple reference points across the surface. This arrangement improves the robustness and precision of the 3D reconstruction process, as the intersecting laser lines generate detailed coordinate data that is critical for accurately mapping surface irregularities. These results not only support existing theories in digital feature extraction but also offer a robust framework for practical applications in manufacturing and quality control, particularly in surface defect detection. The study’s innovative approach advances the field of computer vision, providing new insights and methodologies for optimizing image conversion techniques.