Abstract
The automatic detection and recognition of automotive wheel hubs defects has important significance to improve the quality and efficiency of automotive wheel production and vehicle safety. In order to improve accuracy of detection and recognition of automotive wheel hub defect images, an improved peak location algorithm - trend peak algorithm is proposed to extract region of wheel hub defect, combined with BP neural network to classify and recognize wheel hub defect. Firstly, initial defect positions are extracted using peak locations of vertical and horizontal directions. Then mathematical morphology is used to remove pseudo defects, and the exact locations of the defects are obtained. Finally, the wheel hub defect features are classified to reach the target of defect recognition by BP neural network. In actual industrial conditions, the algorithm is found to obtain good recognition results and reach real-time detection request in low contrast, high noise, uneven illumination, and complex structure of the products, by experiments of X-ray images of four common defects of the actual wheel hubs.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
