Stereo Camera-Based Motor Vehicle Dimension Measurement with Luxmeter-Based Light Intensity Detection
- Ageng Sudarma Politeknik Keselamatan Transportasi Jalan, Tegal, Indonesia
-
Muhammad Iman Nur Hakim
*
Politeknik Keselamatan Transportasi Jalan, Tegal,
Indonesia
- Nurul Fitriani Politeknik Keselamatan Transportasi Jalan, Tegal, Indonesia
- Siti Shofiah Politeknik Keselamatan Transportasi Jalan, Tegal, Indonesia
- Nanang Okta Widiandaru Politeknik Keselamatan Transportasi Jalan, Tegal, Indonesia (*) Corresponding Author
Abstract
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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References
A. B. Sulistyo, T. A. Cundoko, R. R. O. Sasue, R. Ahmad, I. P. A. Suryasa, and A. D. Dwipayana, “Sistem keselamatan bagi awak kendaraan bermotor angkutan barang terminal,” Madiun Spoor: Jurnal Pengabdian Masyarakat, vol. 1, no. 2, 2021, doi: 10.37367/jpm.v1i2.188
S. Li, L. Han, P. Dong, and W. Sun, “Algorithm for measuring the outer contour dimension of trucks using UAV binocular stereo vision,” Sustainability, vol. 14, no. 22, 2022, doi: 10.3390/su142214978.
A. Sianipar, “Analisis distribusi beban pada kendaraan angkutan barang sesuai dengan konfigurasi axle,” Warta Penelitian Perhubungan, vol. 32, no. 1, 2020, doi: 10.25104/warlit.v32i1.1270.
E. Adil, M. Mikou, and A. Mouhsen, “A novel algorithm for distance measurement using stereo camera,” CAAI Transactions on Intelligence Technology, vol. 7, no. 2, pp. 177–186, 2022, doi: 10.1049/cit2.12098.
N. Nurkhowati, S. Widodo, and S. N. Kadarini, “The effect of over dimension overload (ODOL) vehicles on national road damage,” Jurnal Teknik Sipil, vol. 23, no. 1, 2023, doi: 10.26418/jtst.v23i1.59151.
H. S. Nasution, A. Jayadi, and R. Rikendry, “Implementasi metode fuzzy logic untuk sistem pengereman robot mobile berdasarkan jarak dan kecepatan,” Jurnal Teknik dan Sistem Komputer, vol. 3, no. 1, 2022, doi: 10.33365/jtikom.v3i1.1634.
T. Martiana and Y. Purnawati, “Analisis beban kerja dan burnout syndrome pada perawat rawat inap,” UNAIR News, 2020.
K. S. Anwar, O. Z. Tamin, and Najid, “Analisis pengaruh kendaraan over dimension overload (ODOL) terhadap perkerasan jalan,” in Prosiding Simposium Forum Studi Transportasi Antar Perguruan Tinggi, 2020.
M. Vajgl, P. Hurtik, and T. Nejezchleba, “Dist-YOLO: Fast object detection with distance estimation,” Applied Sciences, vol. 12, no. 3, 2022, doi: 10.3390/app12031354.
L. Hamath and M. A. Khan, “Object depth and size estimation using stereo-vision and integration with SLAM,” 2024.
A. Masoumian, H. A. Rashwan, J. Cristiano, M. S. Asif, and D. Puig, “Monocular depth estimation using deep learning: A review,” Sensors, vol. 22, no. 14, 2022, doi: 10.3390/s22145353.
F. Khan, S. Salahuddin, and H. Javidnia, “Deep learning-based monocular depth estimation methods—A state-of-the-art review,” Sensors, vol. 20, no. 8, 2020, doi: 10.3390/s20082272.
Sugiyono, Metode Penelitian Kuantitatif Kualitatif dan R&D. Bandung, Indonesia: Alfabeta, 2015.
A. A. Kozikov et al., “Imaging magnetoelectric subbands in ballistic constrictions,” New Journal of Physics, vol. 15, 2013, doi: 10.1088/1367-2630/15/8/083005.
Z. Larozza, A. Hariandi, and M. Sholeh, “Strategi guru dalam mengatasi perilaku perundungan (bullying) melalui pendidikan karakter pada siswa kelas tinggi SDN 182/I Hutan Lindung,” JIIP - Jurnal Ilmiah Ilmu Pendidikan, vol. 6, no. 7, 2023, doi: 10.54371/jiip.v6i7.1929.
X. Luo, J. Bin Huang, R. Szeliski, K. Matzen, and J. Kopf, “Consistent video depth estimation,” ACM Transactions on Graphics, vol. 39, no. 4, 2020, doi: 10.1145/3386569.3392377.
H. Laga, L. V. Jospin, F. Boussaid, and M. Bennamoun, “A survey on deep learning techniques for stereo-based depth estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 4, 2022, doi: 10.1109/TPAMI.2020.3032602.
I. T. Nurlina, K. Kosjoko, and N. A. Mufarida, “Perbandingan pengereman pengujian rem statis dan pengujian rem jalan pada kendaraan pick up L300,” AutoMech: Jurnal Teknik Mesin, vol. 1, no. 1, 2021, doi: 10.24269/jtm.v1i01.4088.
R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun, “Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 3, 2022, doi: 10.1109/TPAMI.2020.3019967.
S. Ratna, “Pengolahan citra digital dan histogram dengan phyton dan text editor pycharm,” Technologia: Jurnal Ilmiah, vol. 11, no. 3, 2020, doi: 10.31602/tji.v11i3.3294.
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Copyright (c) 2026 Ageng Sudarma, Muhammad Iman Nur Hakim, Nurul Fitriani, Siti Shofiah, Nanang Okta Widiandaru
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