<![CDATA[Over Dimension and Over Loading (ODOL) vehicles require accurate and efficient dimension inspection systems to support transportation safety and regulatory compliance. This study proposes an automatic motor vehicle dimension measurement system based on stereo vision integrated with YOLOv8 object detection and luxmeter-based illumination monitoring. The system was developed using a Research and Development (R&D) approach involving stereo camera calibration, hardware-software integration, experimental testing, and validation against manual measurements. Two Logitech C270 USB cameras with a fixed 50 cm baseline were calibrated using a 9 × 6 checkerboard pattern and processed using OpenCV and Python. Vehicle and wheel objects were detected using YOLOv8 models with stereo disparity estimation performed using Semi-Global Block Matching (SGBM) and triangulation methods to calculate Overall Length (OAL), Front Overhang (FOH), Wheelbase (WB), Rear Overhang (ROH), and Overall Height (OAH). Environmental lighting conditions were monitored using a luxmeter under illumination ranges of 5,000-100,000 lux. Experimental results showed a stereo calibration success rate of 96% from 100 stereo image pairs. The developed system achieved average measurement accuracies of 98.58%, 98.92%, and 98.89% at testing distances of 7 m, 8 m, and 9 m, respectively, while the highest accuracy of 99.44% was obtained at the T_8M_LC configuration under stable illumination conditions. Operational efficiency analysis showed that the automatic measurement process, including image acquisition and computational processing, reduced total measurement time from 187 seconds in manual measurements to 5 seconds in the automated system, corresponding to an efficiency improvement of 97.32%. The results show that the proposed stereo vision system provides accurate, efficient, and lighting-robust vehicle dimension measurements suitable for automated motor vehicle inspection applications.]]>
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