关键词: 类球型水果, 脐橙, 亮度校正, 阈值分割, 缺陷检测

Abstract:

【Objective】 Because of uneven lightness distribution on navel orange surface due to its geometric shape of sphere to oval, it is difficult to segment directly the defect area from the orange image by single threshold method. In order to solve this problem, a novel detection method of the surface defects of navel orange based on the region brightness adaptive correction algorithm was proposed in this study. 【Method】 The Newhall navel orange with high economic value was selected as the research object, and its original image was collected using self-developed image acquisition system. During this experiment, it was found that the gray level of R-B fused component image had obvious bimodal distribution, and the color contrast between the background and the foreground of the original image was relatively large. Therefore, the background was removed using the mask generated by R-B fused component image and the single threshold method. The R-B target image was extracted with the background removed, which could effectively remove the background without damaging the navel orange surface. Then, the brightness information of the target image was calculated based on the region adaptive brightness correction algorithm proposed in this work, and the brightness information was estimated according to the set of brighter pixels in the neighborhood of the points on the target image. In this study, the neighborhood window size was set to w×w, and the value of w was set to 13 by experiment comparison. The pixels in the neighborhood were moved on the target image pixel by pixel, and a new matrix was generated. The new matrix was sorted in ascending order in the column, then the last row was removed to exclude the influence of white noise, and the last five rows were selected and the matrix was obtained by means of column operation. The matrix was inversely transformed and stored. Finally, the matrix image with the same size of the target image, that was, the surface brightness information of the target image was obtained. Based on the obtained luminance information, the R-B fused component image after background removal was corrected by luminance homogenization. After brightness correction, the gray contrast between the defect area and the normal tissue area of the image was relatively large. The single threshold method (segmentation threshold T=194) could be used to extract the surface defect directly from the brightness corrected navel orange image. Finally, the area filter was applied to the binary image after surface defect segmentation to remove the spurious points and noise. 【Result】 The bimodal method could effectively remove the background of the image and preserve the surface information of the target navel orange. All the sample images were also corrected and inspected by using other brightness correction algorithms, including histogram equalization algorithm, Retinex theory algorithm and illumination-reflection theory algorithm. The experimental results showed that the algorithm proposed in this study could correct both the middle and the edge of the normal region of the image, which were to be highlighted and the fluctuation range of the gray scale difference was small, while the defect regions still exhibited low gray level. The average processing time of each image was 0.29 seconds in proposed algorithm. Compared with the other three algorithms, the average processing time of each image could be reduced 0.27 s, 0.14 s, and 1.45 s by proposed algorithm. Finally, the algorithm proposed in this study combined with single threshold method, and it was used to detect the defects of 356 Newhall navel orange samples. Based on this algorithm, 8 kinds of common navel orange surface defects (e.g., canker, thrips, scale infestation, insect injury, blackspot, wind damage, anthracnose, dehiscent) were detected, and the defect recognition rate reached 95.8%. The correct defect recognition rate was improved by 2.6%-8.2% which compared with other three algorithms. 【Conclusion】 The proposed algorithm effectively solved the problem of defect segmentation caused by uneven brightness distribution on the surface of spherical fruits, provided technical support for online accurate classification of navel orange fruits, and provided a new idea for rapid detection of surface defects for other fruits.

Key words: spherical fruit, navel orange, brightness correction, threshold segmentation, defect detection