Recognition of assembly parts based on PXLBP and pixel classification
                                         TIAN Zhongke, CHEN Chengjun, LI Dongnian, ZHAO Zhengxu
                   (School of Mechanical & Automotive Engineering, Qingdao University of Technology, Qingdao 266033, China)

Abstract: Aiming at issue of parts recognition and assembly monitoring in assembly maintenance and guidance of mechanical products, a part recognition and assembly monitoring method based on Pixel Local Binary Pattern (PXLBP) was proposed. Firstly, the classical LBP operator was merged with the pixel classification to propose an improved LBP operator, which is named PXLBP operator. Secondly, PXLBP features of the depth images were extracted. And training set and test set were obtained. Finally, the randomized decision forests classifier was trained. Then the pixel classification of the depth image of the test set was executed to get the pixel prediction image of depth images. The recognition of assembly parts was realized by comparing the RGB values of pixel prediction image and the corresponding color label image. The assembly process monitoring was realized by analyzing the number and position of pixels in pixel prediction image of each assembly part to determine the assembly error. Experiment results show that the method proposed in this paper has high accuracy, realtime and robust ability, and can be used in fields of assembly maintenance guiding, assembly monitoring and automatic assembly.

Key words: part recognition; assembly monitoring; depth image; pixel local binary pattern; pixel classification ；randomized decision forests classifier