Monocular vision and calculation of regular three-dimensional target pose based on Otsu and Haar-feature AdaBoost classifier

Using machine vision to identify and sort scattered regular targets is an urgent problem to be solved in automated production lines.This study proposed a three-dimensional(3D)recognition method combining monocular vision and machine learning algorithms.According to the color characteristics of the targets,to convert the original color picture into YCbCr mode and use the 2D Otsu algorithm to perform gray level image segmentation on the Cb channel.Then the Haar-feature training was carried out.The comparison of feature training and Haar method for Hough transform showed that the recognized time of Haar-feature AdaBoost trainer reached 31.00 ms,while its false recognized rate was 3.91%.The strong classifier was formed by weight combination,and the Hough contour transformation algorithm was set to correct the normal vector between plane coordinate and camera coordinate system.The monocular vision system ensured that the field of camera view had not obstructed while the dots were being struck.It was measured and calculated angles between targets and the horizontal plane which coordinate points of the identified plane feature.The testing results were compared with the Otsu and AdaBoost trainer where the prediction and training set have an error of no more than 0.25 mm.Its correct rate can reach 95%.It shows that the Otsu and Haar-feature based on AdaBoost algorithm is feasible within a certain error ranges and meet the engineering requirements for solving the poses of automated regular three-dimensional targets.

Modeling effectiveness and identification of multi-scale objects in farmland soils with improved Yee-FDTD methods

Finite-Difference Time-Domain(FDTD)is the most popular time-domain approach in computational electromagnetics.Due to the Courant-Friedrich-Levy(CFL)condition and the perfect match layer(PML)boundary precision,FDTD cannot simulate soil medium whose surface is connected by multiple straight lines or curves(multi-scale)accurately and efficiently,which greatly limits the application of FDTD method to simulate buried objects in soils.Firstly,this study proposed the absorption boundary and adopted two typical perfect matching layers(UPML and CPML)to compare their absorption effects,and then using the three forms of improved Yee-FDTD algorithm,alternating-direction implicit(ADI-FDTD),unconditionally stable(US-FDTD)and hybrid implicit explicit finite time-domain(HIE-FDTD)to divide and contrast the boundary model effects.It showed that the HIE-FDTD was suitable for inversion of multi-scale structure object modeling,while ADI-FDTD and US-FDTD were ideal for single-boundary objects in both uniaxial perfectly matched layer(UMPL)and convolution perfectly matched layer(CPML)finite element space.After that,all the models were tested by computer performance for their simulated efficiency.When simulating single boundary objects,UPML-US-FDTD and ADI-FDTD could achieve the ideal results,and in the boundary inversion of multi-scale objects,HIE-FDTD modeling results and efficiency were the best.Test modeling speeds of CPML-HIE-FDTD were compared with three kinds of waveform sources,Ricker,Blackman-Harris and Gaussian.Finally,under the computer condition in which the CPU was i5-8250,the HIE-FDTD model still had better performance than the traditional Yee-FDTD forward modeling algorithm.For modeling multi-scale objects in farmland soils,the methods used CPML combined with the HIE-FDTD were the most efficient and accurate ways.This study can solve the problem that the traditional FDTD algorithm cannot construct non-mesh objects by utilizing the diversity characteristics of Yee cell elements.