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Stereo Görüntüleme ile Araçların Şasi Eğriliklerinin Tespiti

Year 2023, Volume: 11 Issue: 2, 625 - 637, 30.04.2023
https://doi.org/10.29130/dubited.1059019

Abstract

Günümüzde otomotiv sektörü hızla büyümekte ve piyasaya çıkan araç sayısı her geçen gün artmaktadır. Araçların üretim esnasında veya kaza sonrasında meydana gelen şasi eğriliklerinin tespiti ve tamir sürecinin ardından, bu eğriliklerin ne ölçüde giderildiğinin tespiti önem arz etmektedir. Bu çalışmada, iki kameralı ve taşınabilir stereo görüntüleme sistemi kullanılarak görüntü üzerinden araçların şasi eğriliklerinin tespiti üzerine gerçek zamanlı bir yöntem sunulmuştur. Bu amaçla, öncelikle 8 MP kameraların self-kalibrasyonu gerçekleştirilmiş, sonrasında bu kameralar bir levha üzerine paralel olarak sabitlenmiştir. Ardından oluşturulan stereo görüntüleme sisteminin dış parametreleri elde edilmiştir. Ölçüm sırasında test edilecek araç, şasi üzerindeki noktaların tamamının görülebileceği minimum seviyeye bir lift vasıtasıyla kaldırılarak görüntüler elde edilmiştir. Şasi üzerinde belirli 8 farklı nokta, görüntüler üzerinde kullanıcı tarafından işaretlenmiştir. İşaretlenen bu noktalar 3B koordinatlara dönüştürülmüş ve elde edilen koordinatlar aracılığıyla da şasi üzerinde x-y düzleminde ve z ekseni doğrultusunda bulunan eğrilikler hesaplanmıştır. Deneysel çalışmalar sonucunda x-y düzlemi üzerinde 0-2 mm ve z yönündeki eğriliklerde 0-4 mm ölçüm hassasiyeti ile eğriliklerin tespit edildiği gösterilmiştir. Bu sistem ek bir ekipmana ihtiyaç duymadan, özellikle ikinci el araç ekspertiz merkezlerinde hızlı, ucuz ve bireysel test yapmayı mümkün hale getirecektir.

Thanks

Desteklerinden ötürü TAMİŞ SERPAUTO’ya teşekkür ederiz.

References

  • [1]M. H. Doğru, “Çoklu yükleme koşulları altında kamyon şasisinin topoloji optimizasyonu,” El-Cezeri Fen ve Mühendislik Dergisi, c. 2019, s. 3, ss. 856–867, 2019.
  • [2]M. Ayaz, K. Erhan, A. Aktaş, E. Özdemir, ve H. Salihoğlu, “Araç yakıt tankı montajı için otomasyon sistem tasarımı ve uygulaması,” Düzce Üniversitesi Bilim ve Teknoloji Dergisi, c. 3, ss. 357–366, 2015.
  • [3]T. Granata, “Chassis measuring apparatus and method of measuring a chassis,” U.S. Patent 20010052174A1, 2000.
  • [4]J. C. Hodge, “Apparatus and method for wheel alignment, suspension diagnosis and chassis measurement of vehicles,” U.S. Patent 5675515A, 1995.
  • [5]K. Ayaz, İ. Sancaktar, ve S. Karagöl, “Mikroişlemci denetimli beş serbestlik derecesine sahip Robot İle Yüzey Ölçümü,” III. Uluslararası Mesleki ve Teknik Bilimler Kongresi, Gaziantep, Türkiye, 2018, ss. 2211–2218.
  • [6]Nitromac. (2021, December 22). New genereation 3D wheel alignment system and chassis measurement system for heavy duty [Online]. Available: https://nitromac.com/en/urunler/otomotiv/wheel-alignments/wa3056-yeni-nesil-agir-vasitalara-ozel-3d-kamerali-rot-ayar-ve-sase-olcum-cihazi/.
  • [7]Y. Xiao and K. Bin Lim, “A prism-based single-lens stereovision system: from trinocular to multi-ocular,” Image and Vision Computing, vol. 25, no. 11, pp. 1725–1736, 2007.
  • [8]Z. Zhang, “A flexible new technique for camera calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330–1334, 2000.
  • [9]S. Bi, Y. Gu, Z. Zhang, H. Liu, C. Zhai, M. Gong, “Multi-camera stereo vision based on weights,” 2020 IEEE Int. Instrumentation and Measurement Technology Conf. (I2MTC), Dubrovnik, Croatia, pp. 1–6, 2020.
  • [10]J. Cui, D. Feng, C. Min, and Q. Tian, “Novel method of rocket nozzle motion parameters non-contact consistency measurement based on stereo vision”, Optik (Stuttg)., vol. 195, no. July, 2019.
  • [11]Y. Hu, Q. Chen, S. Feng, T. Tao, A. Asundi, and C. Zuo, “A new microscopic telecentric stereo vision system - Calibration, rectification, and three-dimensional reconstruction,” Optics and Lasers in Engineering, vol. 113, pp. 14–22, 2019.
  • [12]T. Jiang, H. Cui, and X. Cheng, “A calibration strategy for vision-guided robot assembly system of large cabin,” Measurement, vol. 163, no. 107991, 2020.
  • [13]A. L. Kaczmarek, “Stereo vision with equal baseline multiple camera set ( EBMCS ) for obtaining depth maps of plants,” Computers and Electronics in Agriculture, vol. 135, pp. 23–37, 2017.
  • [14]Y. Yu, C. Long, and Z. Weiwei, “Stereo vision based obstacle avoidance strategy for quadcopter UAV,” 2018 Chinese Control And Decision Conf. (CCDC), Shenyang, China, 2018, pp. 490–494.
  • [15]C. Wang, Q. Zhang, S. Lin, W. Li, X. Wang, Y. Bai and Q. Tian, “Research and experiment of an underwater stereo vision system,” OCEANS 2019 - Marseille, Marseille, France, 2019 pp. 1–5.
  • [16]F. Oleari, F. Kallasi, D. L. Rizzini, J. Aleotti, and S. Caselli, “An underwater stereo vision system : from design to deployment and dataset acquisition,” OCEANS 2015, Genova, Italy, 2015.
  • [17]Y. Li, P. Chen, and M. Zhang, “The vehicle distance measurement system based on binocular stereo vision,” Proc. of the 2015 Int. Conf. on Electrical and Information Technologies for Rail Transportation, Shandong, China, 2015, pp. 437–444, , doi: 10.1007/978-3-662-49370-0.
  • [18]Y. Li and C. Papachristou, “Road pothole detection system based on stereo vision,” IEEE Nat. Aerospace and Electronics Conf. (NAECON), Dayton, USA, 2018, pp. 292–297.
  • [19]D. Gupta, R. Sharma, V. Basant, H. Jha, and D. Chakravarty, “Autonomous navigation and path planning for ground vehicle in indoor environment,” 2020 7th Int. Conf. Signal Process. Integr. Networks (SPIN 2020), Noida, India, pp. 510–513, 2020.
  • [20]S. Liu, X. Wang, S. Li, X. Chen, and X. Zhang, “Obstacle avoidance for orchard vehicle trinocular vision system based on coupling of geometric constraint and virtual force field method,” Expert Systems with Applications, vol. 190, 2022.
  • [21]C. Guindel, D. Martín, and J. M. Armingol, “Traffic scene awareness for intelligent vehicles using ConvNets and stereo vision,” Robotics and Autonomous Systems, vol. 112, pp. 109–122, 2019.
  • [22]S. Cafiso, A. Di Graziano, and G. Pappalardo, “In-vehicle stereo vision system for identification of traffic conflicts between bus and pedestrian,” Journal of Traffic and Transportation Engineering (English Ed.), vol. 4, no. 1, pp. 3–13, 2017.
  • [23]İ. Sancaktar, “Harici fiksatör uygulamalı kırık kemiklerin tedavisinde otomatik redüksiyon yapabilen medikal robotun gerçekleştirilmesi,” Doktora Tezi, Bilgisayar Mühendisliği Bölümü, Karadeniz Teknik Üniversitesi, Trabzon, Türkiye, 2018.

The Detection of Vehicle Chassis Curvatures with Stereo Vision

Year 2023, Volume: 11 Issue: 2, 625 - 637, 30.04.2023
https://doi.org/10.29130/dubited.1059019

Abstract

Nowadays, the automotive industry is growing rapidly and the number of vehicles on the market is increasing day by day. It is important to determine the chassis curvatures of the vehicles that occur during production or after the accident and to determine to what extent these curvatures are eliminated after the repair process. In this study, a real-time method is proposed for the detection of vehicle chassis curvatures using a portable stereo vision system with two cameras. Firstly, the self-calibration of 8 MP cameras was performed. Secondly, these cameras were fixed in parallel on a plate. Then the extrinsic parameters of the created stereo vision system were obtained. During the measurement, the vehicle to be tested was lifted to the minimum level where all the points on the chassis could be seen, using a lift, and images were obtained. 8 specific points on the chassis are marked by the user on the images. Finally, These marked points were converted into 3D coordinates, and the curvatures on the chassis in the x-y plane and on the z-axis direction were calculated using the obtained coordinates. As a result of experimental studies, it has been shown that curvatures are detected with a measurement precision of 0-2 mm on the x-y plane and 0-4 mm on the z-direction curvatures. Especially in auto expertise, this system makes it possible to perform fast, inexpensive and individual testing without the need for additional equipment.

References

  • [1]M. H. Doğru, “Çoklu yükleme koşulları altında kamyon şasisinin topoloji optimizasyonu,” El-Cezeri Fen ve Mühendislik Dergisi, c. 2019, s. 3, ss. 856–867, 2019.
  • [2]M. Ayaz, K. Erhan, A. Aktaş, E. Özdemir, ve H. Salihoğlu, “Araç yakıt tankı montajı için otomasyon sistem tasarımı ve uygulaması,” Düzce Üniversitesi Bilim ve Teknoloji Dergisi, c. 3, ss. 357–366, 2015.
  • [3]T. Granata, “Chassis measuring apparatus and method of measuring a chassis,” U.S. Patent 20010052174A1, 2000.
  • [4]J. C. Hodge, “Apparatus and method for wheel alignment, suspension diagnosis and chassis measurement of vehicles,” U.S. Patent 5675515A, 1995.
  • [5]K. Ayaz, İ. Sancaktar, ve S. Karagöl, “Mikroişlemci denetimli beş serbestlik derecesine sahip Robot İle Yüzey Ölçümü,” III. Uluslararası Mesleki ve Teknik Bilimler Kongresi, Gaziantep, Türkiye, 2018, ss. 2211–2218.
  • [6]Nitromac. (2021, December 22). New genereation 3D wheel alignment system and chassis measurement system for heavy duty [Online]. Available: https://nitromac.com/en/urunler/otomotiv/wheel-alignments/wa3056-yeni-nesil-agir-vasitalara-ozel-3d-kamerali-rot-ayar-ve-sase-olcum-cihazi/.
  • [7]Y. Xiao and K. Bin Lim, “A prism-based single-lens stereovision system: from trinocular to multi-ocular,” Image and Vision Computing, vol. 25, no. 11, pp. 1725–1736, 2007.
  • [8]Z. Zhang, “A flexible new technique for camera calibration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330–1334, 2000.
  • [9]S. Bi, Y. Gu, Z. Zhang, H. Liu, C. Zhai, M. Gong, “Multi-camera stereo vision based on weights,” 2020 IEEE Int. Instrumentation and Measurement Technology Conf. (I2MTC), Dubrovnik, Croatia, pp. 1–6, 2020.
  • [10]J. Cui, D. Feng, C. Min, and Q. Tian, “Novel method of rocket nozzle motion parameters non-contact consistency measurement based on stereo vision”, Optik (Stuttg)., vol. 195, no. July, 2019.
  • [11]Y. Hu, Q. Chen, S. Feng, T. Tao, A. Asundi, and C. Zuo, “A new microscopic telecentric stereo vision system - Calibration, rectification, and three-dimensional reconstruction,” Optics and Lasers in Engineering, vol. 113, pp. 14–22, 2019.
  • [12]T. Jiang, H. Cui, and X. Cheng, “A calibration strategy for vision-guided robot assembly system of large cabin,” Measurement, vol. 163, no. 107991, 2020.
  • [13]A. L. Kaczmarek, “Stereo vision with equal baseline multiple camera set ( EBMCS ) for obtaining depth maps of plants,” Computers and Electronics in Agriculture, vol. 135, pp. 23–37, 2017.
  • [14]Y. Yu, C. Long, and Z. Weiwei, “Stereo vision based obstacle avoidance strategy for quadcopter UAV,” 2018 Chinese Control And Decision Conf. (CCDC), Shenyang, China, 2018, pp. 490–494.
  • [15]C. Wang, Q. Zhang, S. Lin, W. Li, X. Wang, Y. Bai and Q. Tian, “Research and experiment of an underwater stereo vision system,” OCEANS 2019 - Marseille, Marseille, France, 2019 pp. 1–5.
  • [16]F. Oleari, F. Kallasi, D. L. Rizzini, J. Aleotti, and S. Caselli, “An underwater stereo vision system : from design to deployment and dataset acquisition,” OCEANS 2015, Genova, Italy, 2015.
  • [17]Y. Li, P. Chen, and M. Zhang, “The vehicle distance measurement system based on binocular stereo vision,” Proc. of the 2015 Int. Conf. on Electrical and Information Technologies for Rail Transportation, Shandong, China, 2015, pp. 437–444, , doi: 10.1007/978-3-662-49370-0.
  • [18]Y. Li and C. Papachristou, “Road pothole detection system based on stereo vision,” IEEE Nat. Aerospace and Electronics Conf. (NAECON), Dayton, USA, 2018, pp. 292–297.
  • [19]D. Gupta, R. Sharma, V. Basant, H. Jha, and D. Chakravarty, “Autonomous navigation and path planning for ground vehicle in indoor environment,” 2020 7th Int. Conf. Signal Process. Integr. Networks (SPIN 2020), Noida, India, pp. 510–513, 2020.
  • [20]S. Liu, X. Wang, S. Li, X. Chen, and X. Zhang, “Obstacle avoidance for orchard vehicle trinocular vision system based on coupling of geometric constraint and virtual force field method,” Expert Systems with Applications, vol. 190, 2022.
  • [21]C. Guindel, D. Martín, and J. M. Armingol, “Traffic scene awareness for intelligent vehicles using ConvNets and stereo vision,” Robotics and Autonomous Systems, vol. 112, pp. 109–122, 2019.
  • [22]S. Cafiso, A. Di Graziano, and G. Pappalardo, “In-vehicle stereo vision system for identification of traffic conflicts between bus and pedestrian,” Journal of Traffic and Transportation Engineering (English Ed.), vol. 4, no. 1, pp. 3–13, 2017.
  • [23]İ. Sancaktar, “Harici fiksatör uygulamalı kırık kemiklerin tedavisinde otomatik redüksiyon yapabilen medikal robotun gerçekleştirilmesi,” Doktora Tezi, Bilgisayar Mühendisliği Bölümü, Karadeniz Teknik Üniversitesi, Trabzon, Türkiye, 2018.
There are 23 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Uğur Can Boz 0000-0003-2460-6341

İdris Sancaktar 0000-0002-4790-0124

Publication Date April 30, 2023
Published in Issue Year 2023 Volume: 11 Issue: 2

Cite

APA Boz, U. C., & Sancaktar, İ. (2023). Stereo Görüntüleme ile Araçların Şasi Eğriliklerinin Tespiti. Duzce University Journal of Science and Technology, 11(2), 625-637. https://doi.org/10.29130/dubited.1059019
AMA Boz UC, Sancaktar İ. Stereo Görüntüleme ile Araçların Şasi Eğriliklerinin Tespiti. DUBİTED. April 2023;11(2):625-637. doi:10.29130/dubited.1059019
Chicago Boz, Uğur Can, and İdris Sancaktar. “Stereo Görüntüleme Ile Araçların Şasi Eğriliklerinin Tespiti”. Duzce University Journal of Science and Technology 11, no. 2 (April 2023): 625-37. https://doi.org/10.29130/dubited.1059019.
EndNote Boz UC, Sancaktar İ (April 1, 2023) Stereo Görüntüleme ile Araçların Şasi Eğriliklerinin Tespiti. Duzce University Journal of Science and Technology 11 2 625–637.
IEEE U. C. Boz and İ. Sancaktar, “Stereo Görüntüleme ile Araçların Şasi Eğriliklerinin Tespiti”, DUBİTED, vol. 11, no. 2, pp. 625–637, 2023, doi: 10.29130/dubited.1059019.
ISNAD Boz, Uğur Can - Sancaktar, İdris. “Stereo Görüntüleme Ile Araçların Şasi Eğriliklerinin Tespiti”. Duzce University Journal of Science and Technology 11/2 (April 2023), 625-637. https://doi.org/10.29130/dubited.1059019.
JAMA Boz UC, Sancaktar İ. Stereo Görüntüleme ile Araçların Şasi Eğriliklerinin Tespiti. DUBİTED. 2023;11:625–637.
MLA Boz, Uğur Can and İdris Sancaktar. “Stereo Görüntüleme Ile Araçların Şasi Eğriliklerinin Tespiti”. Duzce University Journal of Science and Technology, vol. 11, no. 2, 2023, pp. 625-37, doi:10.29130/dubited.1059019.
Vancouver Boz UC, Sancaktar İ. Stereo Görüntüleme ile Araçların Şasi Eğriliklerinin Tespiti. DUBİTED. 2023;11(2):625-37.