Abstract
After recording the Vehicle Identification Number (VIN) on the chassis, a procedure of validation is indispensable, to ensure that the code be correctly recorded. The automotive sector utilizes this constantly, trying to eliminate mistakes, once that the VIN is utilized in world scale and these errors bring many troubles to the consumers, and consequently for the brand. In some cases, it is not available, the minimum requirements of reliability necessary for the inspection process. In cases where the inspection is performed manually, with an operator making a visual conferencing, there is risk of commercialize a vehicle with a different VIN of the one contained in the documents, owing a human mistake. This paper proposes the automation of the inspection process, using computational vision in data validation transcribed to the chassis, and low-cost components to read the VIN recorded in the chassis, comparing it to the previously authorized code, to increasing the quality control and avoiding future problems. We subject the system to experimental tests and find efficiencies in the inspection recording VIN. We increased reliability in the process, including a pre-validation of the code to be recorded, where the machine is not authorized to perform recording without requirements validated.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cerere V (2010) Estudo de Medidas Para a Melhoria da Identificação Veicular no Brasil. Dissertação (Mestrado Profissional em Engenharia Automotiva), Universidade de São Paulo
Bertagi V, Bertagi A, Bereza A, Bertagi C, Santos S (2012) Código Padronizado—Chassi. Fundação de Estudos Sociais Do Paraná, Curitiba
Pinheiro M (2000) Variabilidades dos Defeitos do Produto e Desempenho do Inspecionista. Universidade Federal de Minas Gerais—UFTM, Belo Horizonte
Shah P, Karamchandani S, Nadkar T, Gulechha N, Koli K, Lad K (2009) OCR-based Chassis-number recognition using artificial neural networks. Presented at the IEEE ICVES. pp 31−34
Rei J (2010) RFID Versus Código de Barras da Produção à Grande Distribuição. Faculdade da Engenharia da Universidade do Porto, Portugal. pp 30−31
Abubakar M (2014) Implementation of speed up robust feature for detection and tracking of inanimate objects. School of Electronics Engineering, Tianjin University of Technology and Education, Tianjin
Trived S, Gohil S, Bhatt H, Shah P (2014) Benchmarking Niblack’s binarization algorithm using OpenCV library integrated in C++ and FPGA simulation. Institute of Technology, Nirma University, Ahmedabad
Feng M, Tan Y (2004) Contrast adaptive binarization of low quality document images. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore
Gonzalez W (2002) digital image processing. 2 edn. Pearson Education, Singapore
Zeilhofer P et al (2007) Técnicas de reconhecimento de formas para identificação de áreas de irrigação por imagens de satélite. In: Anais XIII Simpósio Brasileiro de Sensoriamento Remoto, Florianópolis, INPE
Rey C, Dugelay J (2002) A survey of watermarking algorithms for image authentication. Eurecom Institute, France
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Souza, L.R.S., Oliveira, R.M.M., Stoppa, M.H. (2015). Proposal of Automated Inspection Using Camera in Process of VIN Validation. In: Ceccarelli, M., Hernández Martinez, E. (eds) Multibody Mechatronic Systems. Mechanisms and Machine Science, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-09858-6_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-09858-6_27
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09857-9
Online ISBN: 978-3-319-09858-6
eBook Packages: EngineeringEngineering (R0)