Size and Defect Detection of Hami Big Jujubes Based on Computer Vision

Abstract:

In order to realize the rapid nondestructive testing for Hami Big Jujubes’ quality detection, a detecting system based on computer vision was established to detect Hami Big Jujubes’ size and defect. The image grabbing card and CCD camera were consisted of the hardware system, which was used to collect image data. Visual Basic6.0 and image processing toolbox of Mil9.0 constituted the software system. The function of MIL9.0 was called in the Visual Basic6.0 to realize the detection. During image processing, the threshold was all chose (0.1,0.7).Many methods were used to identify the features rapidly and get the H value’s mean and variance, such as colour space transformation, mathematical morphology processing and mask etc. Experimental results showed that the correlation coefficient between the projective areas and weights was 0.945.The correlation between projective areas, transverse diameter and vertical diameter was 0.951.The defects grading models were built by BP neural network .The discriminating rate was as high as 99.16％ in training set，and 91.43％ in prediction set. The average testing time was 80 milliseconds, which can satisfy the detection system’s requirements of time．

• Periodical: Advanced Materials Research (Volumes 562-564)
• Pages: 750-754
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.562-564.750
• Online since: August 2012
• Authors: Ben Xue Ma, Xiang Xiang Qi, Li Li Wang, Rong Guang Zhu, Qin Gang Chen, Feng Xia Li, Wei Xin Wang
• Keywords: BP Neural Network (BPNN), Defect, Hami Big Jujubes, Image Processing, On-Line Detection, Size

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