<p>With the rapid expansion of highway networks worldwide, the increasing traffic loads impose significant stresses on nondestructive pavement health monitoring. Rutting, one of critical pavement distress forms, poses a serious threat to traffic safety and accelerates the pavement structure deterioration. While the existing research has primarily focuses on achieving high measurement accuracy under ideal laboratory conditions, its practical application in real-world road inspections faces significant challenges. Chief among these is the presence of motion-induced artifacts, which manifest as distortions along the laser stripe edges. These artifacts interfere with the precise extraction of the pavement’s transverse profile and consequently degrade the accuracy of rut depth measurements. To overcome these limitations, this study develops an automated, line-laser imaging-based rut measurement system as an optical nondestructive evaluation (NDE) tool for robust pavement health monitoring. The proposed workflow integrates global grayscale enhancement with adaptive segmentation to suppress laser stripe artifacts, ensuring clarity of laser strip shape under practical field conditions. A contour tracking–Sobel fusion method, laser center feature extraction, minimum distance-based calibration, and envelope segmentation to suppress motion-induced artifacts in the laser stripe, ensuring its shape remains clearly definable under real-world field conditions. For transverse profile reconstruction and rut depth quantification, a contour tracking-Sobel fusion method is first employed for precise laser centerline extraction. This is followed by application of a minimum distance-based calibration model and an envelope processing method to accommodate diverse rut geometries. System calibration was conducted using laser stripe images collected from three representative asphalt pavements. The results indicated a mean relative measurement error of 9.02% and rut measurement accuracy of 92.09%. Pavement health evaluation based on the Rut Depth Index (RDI) achieved a consistency rate of 84.57%, confirming adaptability and robustness across various traffic and road scenarios. The system features an embedded pre-processing engine for artifact suppression, achieving holistic enhancement of laser stripe definition across both corrupted and clean images with minimal computational overhead. This inherent optimization provides a scalable and field-deployable optical NDE solution for global rut assessment and transportation asset management.</p>