Automatic detection of defective apples using NIR coded structured light and fast lightness correction

文献类型： 外文期刊

第一作者： Zhang, Chi

作者： Zhang, Chi;Zhao, Chunjiang;Huang, Wenqian;Wang, Qingyan;Liu, Shenggen;Li, Jiangbo;Guo, Zhiming

作者机构：

关键词： Machine vision;Defect detection;Stem-end/calyx identification;NIR structured light;Lightness correction

期刊名称：JOURNAL OF FOOD ENGINEERING （ 影响因子：5.354； 五年影响因子：5.144 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： The automated detection of defective apples with a machine vision system is difficult because of the non-uniform intensity distribution on the apple images and the visual similarity between the stem-ends/calyx and the defects. This paper presents a novel method to recognise defective apples by using a machine vision system that combines near-infrared(NIR) coded spot-array structured light and fast lightness correction. By analysing the imaging principle of the spots projected onto the surface of a spherical object, we regard the change in the position of the spots as a coded primitive. A binary-encoded M-array is designed by using primitives as the pattern of the NIR structured light. The stem-ends/calyxes can be identified by analysing a difference matrix from the NIR apple image captured With a multi spectral camera. Fast lightness correction is performed to convert the uneven lightness distribution on the apple surface into a uniform lightness distribution over the whole fruit surface. The candidate defective regions segmented and extracted from the RGB apple image captured with the same multi spectral camera are classified as the true defects or the stem-ends/calyxes by using the result of the stem-end/calyx identification in the NIR image. The apples are finally classified into sound and defective classes according to the existence or absence of defects respectively. The online experimental result with an average overall recognition accuracy of 90.2% for three apple varieties indicates that the proposed method is effective and suitable for defective apple detection. (C) 2017 Elsevier Ltd. All rights reserved.

分类号： TS2

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